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Dilip Sahay

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IETE TECHNICAL REVIEW

The Institution of Electronics and Telecommunication Engineers

The IETE Technical Review invites readable articles, preferably without mathematical expressions, state-of-the-art review paperson current and futuristic technologies in the areas of electronics, telecommunication, computer science & engineering, informationtechnology (IT) and related disciplines. In addition, informative and general interest articles describing innovative products &applications, analysis of technical events, articles on technology assessment & comparison, new & emerging topics of interest toprofessionals are also welcome. While all the papers submitted will go through the same detailed review process, short papers andPractical Designs will receive special attention to enable early publication. For submission of articles, please see 3rd cover of thisissue. Detailed guidelines to authors may be seen on IETE Website : http://www.iete.org under the heading ‘Publications.’Annual Subscription : Subscription and Advertising rates are available on request and also on website: iete.orgAddress for correspondence :Managing Editor, IETE, 2, Institutional Area, Lodi Road, New Delhi 110 003, Telephone : +91 (11) 43538841-45Fax : +91 (11) 24649429, email : [email protected]; [email protected], Website : http://www.iete.org; http://www.iete.info

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Vol. 25, No. 3, May-June’08 I E T E T E C H N I C A L R E V I E W

IETE TECHNICAL REVIEWPublished bimonthly by the Institution of Electronics and Telecommunication Engineers

May-June 2008 Vol 25 No 3

CONTENTS

Note : The Institution of Electronics and Telecommunication Engineers assumes no responsibility for the statements andopinions expressed by individual authors.

97 SCAN

Dilip Sahay

99 An Automated Beam ExtractionSystem for Microtron

A M Khan, Mohammad Mahfooz Sheikh,Ganesh, B Hanumaiah and K Siddappa

105 Semantic Web Service Composition

Sandeep Kumar and R B Mishra

123 Corner Detection Algorithms for DigitalImages in Last Three Decades

Ambar Dutta, Avijit Kar and B N Chatterji

135 SPIHT: Highly Efficient Technique forImage Transmission and Coding

Nilkanth B Chopade and A A Ghatol

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IETE Technical journal has made a few changes. Cover page design hasbeen changed to have a pleasing standard format. IETE headquarters is processingfor online submission and review of articles in IETE journal. The publicationdepartment is putting in lots of efforts to make the IETE Technical Review of goodstandard which may be accepted by readers as well as internationally.

In this issue of Technical Review, there are four articles.

The first article by A M Khan et al is regarding a computer based systemdeveloped for a computerized control of a specific Beam Extraction System. Thiswas designed and tested according to the specification of the Beam ExtractionSystem. The authors claim that this design system has improvement over theexisting one. Beam Extraction System which is explained in this article is forextracting electronic beam from desired orbit which is simple to operate remotelyand cost effective.

The second article is on Semantic Web Service Composition. This articlereviews some of the popular semantic web service composition method andpresents the same in tabular format. The systems based on semantic web requireperforming many processes. However, in this article, authors Sandeep Kumarand R B Mishra primarily deals regarding sementic web service compositiontechniques.

Third article in this review is by Ambar Dutta et al. The paper present literaturesurvey of existing Corner Detection Algorithms developed during the last threedecades.

The fourth article in this Technical Review is by Chopade and Ghatol whichpresents wavelet based coding algorithm, SPIHT for coding and compressing animage mail data. The authors claim that this coding and decoding process iscomparatively fast.

Dilip SahayChairman, Editorial Board

SCAN

99


An Automated Beam ExtractionSystem for Microtron

A M KHAN, MOHAMMAD MAHFOOZ SHEIKH, GANESH,B HANUMAIAH AND K SIDDAPPA

ABSTRACT

In this paper we report on a computer based system developed for the automated control of the Beam ExtractionSystem (BES) of Microtron (electron accelerator facility at Mangalore University). The BES helps in extracting theelectron beam from a desired orbit. The automated system is designed with two levels. At the system level, there is amicrocontroller along with some circuit components to place the mouth of the extraction channel at a particular orbitso that an electron beam of a particular energy can be extracted from the microtron for experimental studies. Thecomputer kept in the control room away from the microtron machine (i.e. user level) passes the orbit number fromwhich the beam is to be extracted to the microcontroller. This automated system is very simple, easy to operate andcost effective. It also helps in accurate setting of extraction channel and extraction of electron beam of desiredenergy. The work described here is an attempt to improvise on the existing system and computerise the same foraccurate settings and control from a remote distance.

INTRODUCTION

Microtron is an electron accelerator facility atMangalore University which accelerates electronsto an energy range of 4-12 MeV. There are variousunits which help in the operation of the microtronand all these units are controlled using a controlsystem [1]. The electrons are emitted from theelectron emitter when it is heated to a temperatureof 1500 °C to 2000 °C. These electrons travel in themicrotron cavity in a circular path of increasingradii called orbits, under the influence of magneticfield. An RF power is applied to the cavity toaccelerate the electrons. On the application of eachRF pulse, the electrons jump into a circular pathwith a greater radius. Thus, an electron beam orbitsin a circular path with increasing radii having acommon tangent at the emitter. The energy ofelectron beam is different in different orbits and itincreases with increase in radius of the orbit. Thecross sectional view of the beam pattern exhibitedin the microtron is as shown in Fig 1. There aremany units in the microtron system which have tobe controlled and monitored for proper operation ofmicrotron [2-4]. One of the units of microtron whichhelp in extracting the electron beam is the BeamExtraction System. The beam extraction system ofMicrotron has been designed to extract electronbeam from any of the 14 orbits [5]. This beam of

electrons is extracted from different orbits bymeans of an extraction mechanism which consistsof an extraction channel driven by two steppermotors. The work described here emphasizes onthe design and development of an automated controlsystem to extract the electron beam from thedesired orbit. The extraction of the electron beamfrom different orbits gives the electron beam withdifferent energy levels. The positioning of the mouthof the extraction channel at the proper orbit isessential in order to gain optimum energy of theextracted beam.

SYSTEM AND ITS AUTOMATION

The existing system consists of bulky circuitsand the positioning of the mouth of the extractionchannel is done with the help of a stepper motorcontrol mechanism for both X- direction and Y-direction. The distance (in mm) which the mouth ofthe extraction channel must move in order to extractthe beam from the desired orbit is entered manuallywith the help of a key pad. The driver circuit is thusenergised to rotate the stepper motor and move themouth of the extraction channel to the desired orbit.With the help of ‘Inching Mode’, the stepper motorcan be moved in single steps to compensate anyerror in the extraction of the optimum energy of theelectron beam from the desired orbit. This tedious

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manual operation can be minimised by theautomation of the above described system.

Since the orbits of the electron beam are fixedand electrons in every orbit have a definite energy,we can place the mouth of the extraction channel inthat orbit and extract an electron beam of requiredenergy. The required energy value is entered by theuser in the remote computer. The simple c codeaccepts the value and converts it to the orbit numberwhere the specified energy can be obtained. Thepreviously set orbit is the reference and is alwayskept in the data file. The newly computed orbitnumber is compared with the reference orbit numberand the difference is computed. This differencedata is passed to the MCU. The MCU receives thedata from the remote computer and energises thedriver circuits to rotate the stepper motor and placethe mouth of the extraction mechanism at therequired orbit.

Data from the computer carries the informationas to which orbit has to be selected for theextraction of the electron beam. This data is sent tothe microcontroller through the parallel port of the

computer. The microcontroller receives data fromthe computer and executes the microcontrollerprogram accordingly to rotate the stepper motors.These stepper motors are connected to the mouthof the extraction mechanism by means of a longshaft that goes inside the microtron cavity. Thus bycontrolling the angle of rotation of the steppermotors, the shaft is moved inward or outward toposition the mouth of the extraction channel in anappropriate orbit. Since the rotation has to be in thehorizontal X direction and angular Y direction, twomotors are used to move the mouth of the extractionchannel. The number of steps the stepper motorrequires to move the extraction channel from oneorbit to another are stored in a look - up table in themicrocontroller. Hence, the microcontroller movesboth the X and Y stepper motors by the requirednumber of steps to place the mouth of the extractionchannel in the desired orbit.

CIRCUIT DESCRIPTION AND WORKING

The Fig 2 shows the block diagram of thecontrol system used to control the beam extraction

Fig 1 Cross sectional view of the electron beam pattern in the microtron

1 - Mouth of the Extraction Channel.2 - Shaft to move the mouth of theextraction channel with the steppermotors.3 - Microtron cavity where electronbeam pattern is exhibited.

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system of microtron. The microcontroller used isAT89C51 which is an Atmel make, low power, highperformance, 8-bit CMOS microcontroller with 4kBof flash Programmable and Erasable Read OnlyMemory (PEROM). It has 128x8 bit internal RAM,32 programmable I/O lines and two 16-bit timers/counters. The on-chip flash permits the programmemory to be reprogrammed by an in-systemprogrammer or a conventional non-volatile memoryprogrammer.

This combination of 8-bit CPU with flashmemory on a monolithic chip is a powerful, highlyflexible and cost-effective solution to manyembedded control applications [6,7]. The controllogic consists of an up/down counter and a decoder.The CMOS technology up/down counter CD4029 isused to count the clock for increasing the steps ofthe stepper motor. This counter is controlled by asingle control pin for up/down counting. The TTLseries 74LS155 converts the binary count from thecounter to its decimal equivalent. The steppermotor being a sequential device requires the binarycount of the counter to be decoded to its decimalvalue. The output of the decoder is connected to thedriver circuit. Thus, the up/down count of thecounter is converted to sequential switching of thedriver circuits that rotate the stepper motors. Anexternal synchronisation clock circuit has beenused to provide synchronised clock for the timer/

counter of the microcontroller as well as the up/down counters.

The stepper motor is connected to the controllogic that causes its rotation, through the motordriver circuit. The stepper motor used has a 28kg.cm torque with a 1.8 degree step. Thus a heavydriver circuit (capable of handling approx. 1.5A) isrequired to rotate the stepper motor. Thisrequirement is accomplished by using the 2N3055transistors in the driver circuit. These powertransistors can draw current up to 15A without anydestruction. The four transistors are switched on/off sequentially by the control logic to drive the fourcoils of the stepper motor. When the transistorconducts, 5V (Vcc) is applied to the motor coils andcurrent flows through them creating magnetic fields.The magnetic field energy thus created is stored inthe coils and rotates the motors. When thetransistor stops conducting, power to the coils iscut-off and the stored magnetic field collapses. Dueto the collapsing of the magnetic field a reversevoltage (called inductive kick-back or back EMF) isgenerated in the coils. The back EMF can be morethan 100 Volts and can destroy the coils itself byburning them. Thus, diodes are required to beconnected across the coil in reverse direction toabsorb the reverse voltage spike. This voltage ifnot absorbed by the diodes, may produce opposite

Data fromComputer

MCU

SynchronisationClock

CONTROLLOGIC

DRIVERSTAGE

Fig 2 Block diagram of control system

StepperMotors

X

Y

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Send thecomplement of the

difference to themicrocontroller

Is

the

difference

negative?

Save the new data

in the file for next

reference

Get the input data for

new position

Retrieve the old data

from the file and find

the difference

Send the difference

to the

microcontroller

Fig 3 Flow diagram of the computer program

torque and cause improper rotation of the motorand also damage the transistors. Any type ofrectifier or switching diodes of appropriate currentrating and reverse voltage breakdown rating can beused. However the commonly available diodes the1N4007 have been used here.

The AT89C51 microcontroller is programmedusing Atmel’s flash programmer. One step rotationof the stepper motor used in the circuit is 1.8o.When the motor is programmed for 200 steps, itmakes one complete rotation, i.e. 360o. The look-up table is maintained at address location f000h tocount the steps of rotation for X and Y directionmotors. The data is checked for forward / backwardrotation by checking the MSB of the data. If theMSB is “0” it is for forward rotation. Else if the MSB

is “1”, the data is for backward rotation. The requiredcount is loaded into the timer /counter registers ofthe microcontroller that generates the requireddelay. This delay at the microcontroller pin o/penables the up/down counter to count the steps ofrotation of the stepper motor. This count isconverted into a sequence of pulses that rotate thestepper motor. The Figs 3 and 4 show the flowdiagrams of the programs for the working of thesystem.

RESULTS

After a careful study of the requirements andspecification of the Beam Extraction System of themicrotron, the circuit described above wasdesigned, tested and was found to operate

Yes

No

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Complement the offsetvalue.

Check

if

MSB = ‘1’ ?

Read the offset value forrotation from the computer

port.

Load corresponding count fromthe look-up table to

timer/counter

Fig 4 Flow diagram of microcontroller program

Yes

No

Give enable signal and reset aftercounter interrupts.

Return

according to the required specifications of themicrotron. This circuit was first tested by simulatingit using an 8031 microcontroller kit and later theprogram was burnt into AT89C51 microcontroller.Thus, an automated control system for extractionof electron beam of desired energy in the microtronhas been constructed. This system is more flexiblesince the user can directly enter the value ofelectron energy. The control can be done even froma remote distance. The designed circuit is animprovement over the existing circuit where thedistance by which the mouth of the extractionchannel has to be moved is entered manually.

ACKNOWLEDGEMENTS

We acknowledge the help rendered by ShriYogendra Sheth, Scientist, CAT, Indore and Dr.Ganesh, Microtron Centre, in providing the technical

information and support. We also thank the technicalstaff of the Microtron Centre, Mangalore University, fortheir help and support during every phase of the work.

REFERENCES

1. Y Sheth & B J Vaidya, Control system for Microtrona Mangalore University, Proc. of the Intl. conferenceon R&D using accelerators, p 103, September 1995.

2. A M Khan, Mohammed Mahfooz Sheikh, BHanumaiah, Ganesh & K Siddappa, Design of aComputer based Control System for the CathodePower Supply of Microtron, Proc National Symposiumon Instrumentation (NSI) - 30, p 165, Nov-Dec 2005.

3. A M Khan, B Hanumaiah, Ganesh & K Siddappa,Design of a control card for remote operation of themagnet power supply of microtron, Proc of theNational Symposium on Radiation Physics (NSRP) -15, p 18, Nov 2003.

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4. A M Khan, Mohammed Mahfooz Sheikh, BHanumaiah, Ganesh & K Siddappa, Design of aComputer based Control System for the MagnetronPower Supply of Microtron, Proc. NationalConference on Emerging Trends in EmbeddedSystems, p 1, Oct 2005.

5. S P Kapitza & V N Melekhin, The Microtron, 1st edition,Hardwood Academic Publishers, London, 1969.

6. Muhammad A Mazidi & Janice G Mazidi, The8051 microcontroller and embedded systems,Fifth Indian Reprint, Pearson Education Inc.,Delhi, 2003.

7. Kenneth J. Aiyala, The 8051 microcontrollerarchitecture programming and applications, Thirdedit ion, Penram Intl Publications, Mumbai,2003.

Authors

A M Khan obtained his MSc in Applied Electronics from Gulbarga University and his PhD fromMangalore University. He is presently Reader & Chairman, Dept of Electronics, MangaloreUniversity. He has teaching and research experience of around 15 years. His research interestsinclude embedded systems, biomedical electronics and communications.

Address: Department of Electronics, Mangalore University, Mangalagangori 574 199.

email: <[email protected]>

* * *

Mohammad Mahfooz Sheikh obtained his MSc in Electronics from Mangalore University. He ispresently working as a research fellow in the Dept of Electronics, Mangalore University. He hasalso worked at National Aerospace Laboratories, Bangalore as a graduate trainee. He is alsopursuing his PhD studies in the area of optical switching.

Address: Department of Electronics, Mangalore University, Mangalagangori 574 199.


* * *

Ganesh Sanjeev obtained his MSc and PhD in Physics from Mangalore University. He ispresently working as senior physicist at Microtron Centre, Mangalore University. He hasresearch experience of over 15 years. He is involved in all the research activities associatedwith the Microtron Centre.

Address: Microtron Centre, Mangalore University, Mangalagargotri 574 199.


* * *

K Siddappa is former Director, Microtron Centre and former Vice-Chancellor, BangaloreUniversity. He is presently the Honorary Director, JSS Foundation for Science and Society,Bangalore. He has teaching and research experience over three decades.

Address: Microtron Centre, Mangalore University, Mangalagargotri 574 199.

Paper No 13-B; Copyright © 2008 by the IETE.

105


Semantic Web Service CompositionSANDEEP KUMAR AND R B MISHRA

ABSTRACT

The research community of Web, presently is working to generate its next generation i.e. Semantic Web. They aremoving towards automation of the retrieval and processing of web contents. The systems based on the semantic webrequire performing various processes like discovery, selection, composition, orchestration, monitoring etc. onservices provided by it for satisfying client needs. In this paper, we will be concentrating on semantic web servicecomposition techniques. The work deals with exploring different type of composition techniques, categorization ofthem, and comparing them based on some of their properties like process, reasoners & languages involved, interfaceetc.

1. INTRODUCTION

The Semantic Web is not a separate Web but anextension of the current one, in which information isgiven well-defined meaning, better enablingcomputers and people to work in cooperation, byTim Berners-Lee in Scientific American, 2001. Theaim of semantic web is to create a layer on theexisting web that enables advanced automaticprocessing of the web contents so that data can beshared and processed by both humans andsoftware. It is using the concept of self-describing,machine-readable knowledge which is accessibleusing standard web programming constructs.Semantic web services (SWSs) are generally self-sufficient, reusable software components whichcan be used to fulfill a particular task. They havemodular structure and can be published and invokedthrough the web. However in some cases, thesemantic web based systems can not satisfy theclient requirements using only the single servicecomponents. In those cases, discovery andselection is used for selecting the most appropriateservice components followed by servicecomposition for generating the aggregation ofavailable service components according to therequested task. The service composition processcan involve composition of homogeneous as wellas heterogeneous services. The interface,properties, and capabilities of the semantic webservices are encoded in a machine-understandableform allow easy integration of heterogeneoussemantic web services.

The research on semantic web servicecomposition techniques based on variousapproaches is going on. Some of the approachesused for service composition are AI (ArtificialIntelligence) planning based, workflow based,ontology based, agent based, context based,template based etc. Various techniques based uponthese approaches are proposed by variousresearchers. Following points have been coveredin this paper.

1) Categorization and exploration of somesemantic web service compositiontechniques.

2) Comparison of different compositiontechniques about some of their propertieslike process, reasoners & languagesinvolved, interface etc.

3) Observations on service compositiontechniques.

The paper has been structured as: Apart fromintroduction, section 2 provides categorizeddescription of different semantic web servicecomposition approaches. Section 3 deals with thetabular comparison of various service compositionmethods. Section 4 presents some observations ondifferent composition methods and conclusion isgiven in section 5.

2. SWS COMPOSITION APPROACHES

We have categorized the different SWScomposition techniques discussed here into five

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groups based on their corresponding approach usedi.e. AI planning based, workflow based, ontologybased, agent based and other approaches. Otherapproaches consist of approaches like iterative,logic based, template based, interactivecomposition methods, multilevel Composability.

2.1. AI Planning based

The AI planning based service compositionconsists of firstly generating the plan forcomposition before performing the actualcomposition.

Java based Automatic Composition

A Java based completely automatic semanticweb service composition approach [1], not requiringany human-intervention, has also been developedwhich uses planning-based approach for handlingprotocol heterogeneity problem and service-basedapproach for handling data heterogeneity problem.The loop generation in planning is based on thepattern-based approach. The system extendsGraphPlan, an AI planning algorithm, for generatingthe control flow of processes by considering thestructure of input and output messages in additionto the precondition and effects of operations. Thesystem generates executable workflow directly inBPEL using IBM BPWS4J API and which is run onOracle BPM engine. The available message to beinput in the BPEL process is converted into therequired format using data mediation service.However, if the available message has more thanone candidate element, the best one is selected byusing a context-based ranking algorithm. Theontologies in the system is handled using Jena anduses matching or mapping techniques for handlingthe multiple ontologies involved. This technique isvery flexible in adapting to the new scenario, as itonly needs to adapt the task specification anddiscovery rules & preferences while moving to thenew scenario.

WS-GEN

An AI planning based tool, WS-GEN [2] forautomated composition of semantic web services,which takes as input the description of set ofavailable services and business requirement isalso available. It supports OWL-S and BPEL4WSfor describing and interacting with the web services

respectively. This tool exploits AI planningapproach based on the “Planning as Modelchecking” framework [3] and MBP tool [4]. WS-GEN is composed of five software modules –OWL2STS, BPEL2STS, COMPOSE, MBP Planner,and STS2BPEL.The tool firstly describe the existingweb services specified in OWL-S or BPEL4WS intostate transition system using first and secondsoftware modules. Then these described statetransition systems for the existing web servicesare used by the COMPOSE module for compositionof goal service. The MBP planner is used togenerate a plan to control the interaction withexternal existing services in such a way that theresult is according to the goal service. STS2BPELthen translate the result described in state transitionsystem form into BPEL4WS executable processand also provide information for execution anddeployment of resulting processes.

CLM Formal Model

An AI planning-oriented formal model [5],Casual Link Matrix (CLM), for functionalcomposition of the semantic web services hasbeen used. This model uses a regression basedapproach for composition of web services usingRa4C algorithm and perform composition of webservices by automating the process of chaining ofweb services based on their functional descriptionaccording to the casual link (a semantic link). Thecasual link gives a logical dependency among inputand output parameters of different web services.The CLM pre-computes all casual links betweenweb services as an Output-Input matching. Then itstores all these casual links in order to find the bestweb service composition. Out of this, asemantically well ordered and linked plan of webservices forms the solution of web servicecomposition.

2.2. Workflow Based

Workflow based service composition consistsof a series of work-items and data dependencyamong them in the form of a process model. Here,firstly the appropriate atomic services are searchedand then these work-items are filled by thecorresponding atomic service and data & control-flow connections are established between them toproduce the goal service.

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OWL-S Process Model

An OWL-S process model using workflows [6]for composition has been proposed. The modeldescribes the required composite services in theterms of other constituent services. This model isbased on the DAML-S Virtual Machine MatchmakerArchitecture. All the processes in the environmentare categorized into three classes as Atomic,Simple, and composite. In this model, thecomposition process is defined as a hierarchy ofworkflows. The workflows are constructed usingatomic, simple or other composite processes usingvarious composition constructs like if-then-else,repeat-until, repeat-while, iterate, split, split+join,sequence, unordered, choice. The model can beused reasoning of the possible compositions andalso to control the invocation of services. It alsouses a special application tool WSDL2DAMLS.

Knowledge based Approach

A knowledge based semantic web servicecomposition approach [7] implemented in aworkflow construction environment has beenproposed which uses domain knowledge for servicecomposition, selection and instantiation process.The system generates workflows which can beexecuted using a domain-specific direct mappingmechanism as well as using WSDL-based servicegrounding. For generating the workflowspecifications, in addition to the domain knowledge,it also uses semantically enriched servicedescriptions which assist in the process ofdiscovery which further leads to the workflowspecifications. A framework implementing thisapproach consists of a Workflow ConstructionEnvironment, a set of web services, knowledgebases and ontologies. The framework mainlyemphasis the use of DAML-S for representing theservice descriptions.

Information transformation on I/Oparameters

A composition approach [8] in which services iscomposed into workflows has been proposed. Inthis approach, the composition into workflows isbased on the information transformation theworkflows undertake in their input/output

parameters. The workflow starts by finding all theservices with the input matching with requestedinput. Then the services are added into the workflowwith the inputs matching with the outputs of the lastservice in the workflow. This process of addingservices to generate a workflow-graph with eachbranch representing a workflow continue until nomore matches among the unused services can befound or the outputs of the last service in theworkflow match with the requested outputs. It is tonote that the each branch holds its own list ofunused services. The process described here is inforward chaining fashion. However, this approachalso allows composition using backward chainingfashion. The output here can be conditional as wellas unconditional. In the case unconditional outputis there, both the forward as well as backwardchaining approach proves equally good, but in thecase of conditional output, the backward chainingapproach is usually used.

OntoMat- Service Infrastructure

An OntoMat-Service infrastructure [9] which inaddition to having easy to use user-interface alsoprovides capability to generate reasonably complexworkflows has been developed. It uses a particulartype of semantic annotations called deepannotations [10]. The OntoMat-Service browserprovided by it has enhanced features like direct,manual invocation of an advertised service,invoking aggregated web services. The OntoMat-Service system has web services with the servicedescription using WSDL (Web Service DescriptionLanguage). The web services are presented beforethe user in the form of a nicely formatted HTML/XHTML document, which can be seen of the user atthe client side using OntoMat-Service browser.This browser also uses deep annotations tohighlight the human-understandable itemsassociated with underlying machine-understandable semantics. The user at the clientside can not only view the web services but also themapping rules. User here selects a set of webservice operations and mapping rules. Theseselections are then used by the web service plannermodule to compute the possible web serviceworkflows using pre-conditions and post-conditionsof web services. However, this model is not muchefficient for building the complex workflows.

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2.3. Ontology Based

Ontology based service composition involvescomposing services based upon their ontologicaldescriptions and relationship between them.

Stack of Ontologies based framework

A framework which can be used for design andautomatic/semiautomatic composition of thesemantic web services using ontologies [11] hasbeen developed. The composition process here isbased on a stack of ontologies describing differentparts of semantic web services and containingdesign rules to be verified by the ontologyinstances. The framework consists of mainly threemodels- Instance Model, Checking Model (forchecking consistency among the instance models),and translate model ( for translating ontologyinstances into semantic web languages such asOWL-S). For the composition of the services, thefollowing steps are performed: designing servicesgraphically, creation of the instances of allframework ontologies, checking the design ofservice ontologies, and then translating theinstances into OWL-S specifications. Thisframework provides composition of semantic webservices at language- independent and knowledgelevel. Also for performing the translation ofontologies and inference procedure, a specialsoftware packages WebODE is used. A graphicsinterface packages ODE SWS [12, 13] integratedwith WebODE is used for producing the graphicaldesign of services.

Composition of WSMO-based ontologiesin IRS-III

Internet Reasoning Service-III (IRS-III), aframework for the creation, publication,composition and execution of semantic webservices has been implemented [14]. IRS-IIIsupports composition of services according to theWeb Service Modeling Ontology (WSMO) ontology[16], which also takes into account the notion ofgoal and mediation. However a graphical softwaretool [17] has been developed in Java which supportsIRS-III in dynamic composition by re-commandinggoals according to the context at each step ofcomposition. This tool also performs orchestrationon the generated compositions.

Integration of OWL-S into IRS-III

Another approach of composition of semanticweb services described in OWL-S using IRS-III hasalso been proposed [15]. As IRS-III obeys thenotion of goal for describing web services, so theOWL-S description has to be converted into anotion that uses notion of goal. So, here, the OWL-S ontology specifications are firstly mapped to theWSMO (Web Service Modeling Ontology) ontology[16] which uses notion of goal, and then theseWSMO ontologies are translated into OCML whichare used for IRS-III. The composition process hereinvolves composing services dynamically bycapturing only some of the functionalities fromeach of the services.

2.4. Agent Based: Functionality andMethods

In agent based service composition, thedifferent agents involved in the system representthe differernt individual services. Then a completemulti-agent based system can be considered forcomposition of semantic web services.

Agent functionality encompasses agent acts(agent per formatives), platform for agentcommunication, and languages. The social settingsand mental states of agent are the two paradigmswhich speak of the various activities. Cooperation,Coordination, and negotiations are acts of socialsettings in the multi-agent system. The mentalstates of the agents have been enumerated asbelief, knowledge, commitment, capability, andchoice [18]. BDI theory depicts of the belief, desire,and intention as prime entities to model the behaviorof agent during plan, goal, and action. BDI theory isconcerned about the subjective internal based andinstitutional model of an agent act. But there arecertain parameters explicit external and objectivebased on the internal or institutional parameterssuch as trust, reputation, and reliability. Theseparameters are eternally correlated to each other tothe internal states.

Trust: Trust is an ethical and functional issue tomodel the behavior of agents during interaction andcommittment to coordinate, cooperate, and cotrolpolicies between the agents and web servicesproviders. Researchers in [19] describe the trust in

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multi-agent systems. In the trade on semantic web,trust and reliability play very important role. Trustbe transitive, subjective and context dependent.Trust and reputation are made of underlying beliefsand are the functions of these beliefs. Integration oftrust and belief into query optimization, transpatterns as an additional intention for optimizationeither as a constraint or as a quantitative trade off.To determine appropriate organizational policiesinfrastructures with a GUI’s for composing,updating, importing, protecting and analyzing andthen automatically mapping that high levels policyto suitable informant mechanism and to semanticand security models that partner organization use.

Negotiation: FIPA Iterated contract Net protocol isan interaction protocol for negotiation which anInitiator finds which participants from the parts ofparticipant would be able to perform the activities.Initiator and participants are the SRA and SPArespectively. There are several round in thecontraction, which is obtained by matchmakersagents. The possible contract is almost the term ofpossible contract in which initiator advertises theactivity input and consequently P’s (contractor)proposals are obtained. In final round of negotiation,I consider the P’s agrement and the contract deal.P is a contractor and generates coalition of agentsto perform the task. RaCING(Rational AgentCoalitions for Intelligent Mediation of InformationRetrieval (http://www.zsu.zp.ua /racing/project )presents a frame for the development anddeployment of mediator which do the servicecomposition and mobile age service representationin a P2P network of service integration platform.

Coordination: The agents are suppoted to worktogether in coordination to achive certain goals.Even the agents are not designed to work incoordination, they can transfer data amongthemselves. When data is presented semanticallywhich is performed by semantic markup,coordinating agents perform mapping between theirknowledge in semantic web. This is implemented inOWL-S or the use of partial mapping. Dialogueprotocol is a layer of control above the performativelanguage that defines only how agentscommunicate where as the dialogue protocols howagents can communicate. The social commitmentbetwewen agent is a binding agreement from oneagent to another by establishing sharedcommitments between agents as a social policy to

control their interaction.

P2P based Multiagent

A multiagent system [20] which is based on theChord P2P network [21] has been proposed forcomposition as well as discovery of semantic webservices. The system provides dynamiccomposition of services using agents whichcooperatively apply distributed symbolic reasoning.The semantic web ontology language OWL-S isused in the system for repsenting the services. Forthe composition of of web services, firstly, theavalibale semantic web services are taken as inputfrom OWL-S service profile. Then the inputservices are transformed into Linear Logicformulae. Then partial deduction is applied on thelinear logic formulae to get the requested solution.The final solution is transformed into the OWL-Sservice profile and returned to the requester. Thesystem provides a distributed composition ofsemantic web service by applying agenttechnologies on structured P2P networks. Thereasoning over the services is performed usingdomain ontologies during the partial deductionprocess. Also in the case, the solution obtained ispartial and not complete, then this can also beextended using a Cooperative Problem SolvingFramework to get the complete solution.

Composition Engine

A Composition Engine which uses DAML-S,scheduling of tasks, workflow planning, executionprocess status monitoring, faults handling andcommunication with entities like agents andregistries for automated composition has also beenproposed [22]. This engine consists of a set ofmodules like Planner, Definer, Scheduler,Executer, Reasoning and Communication module.Definer generates new DAML-S specifications fromthe individually composed service specifications.The communication module of the system usesJava Agent Development Framework (JADE) [23]for communicating with user agents or othercomposition engines. The reasoning module uses aJava based object oriented modular reasoningsystem, JTP (Java Theorem Prover) [24].

Agent Enabled Framework

A framework using agents for intelligent

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dynamic composition which can cop with changesin environment affected by service execution flowhas been proposed [25]. In this system, an agentbased middleware layer, as a service mediator, isrequired for scalable intelligent dynamic servicecomposition interpreted as tasks comprising ofvarying granularity. It is composed by dynamiccoalitionof service providing agents (SPA) in theparticipating task.It basically decomposes theincoming task on the basis of their local knowledge/ontology. Based on iterative extended iterative netnegotiation arranged for activity outsourcing toother SPAs by providing service context relocation.However there are some of main issues stillunsolved or partially solved as like no commonmechanism for activity outsourcing activityparameters for adjustment meaning of negotiationontologies family, unsufficient methodology forrepresentation of task /activities /services dynamicstructure at coarse level and task/processontologies with fine granularity, non standardizationof measure, assesment and evaluation of humanbase parameters such as trust, crediability,reliability andcapability. In the system , semanticfact of request task activity service layering activitycontext is translated with DAML-S markupcorressponding to service profile; the service isthan invoked via the internet specified by its bindingi.e. grounding in DAML-S description.

Interactive Agents

The agents in semantic web services are usedas clients (requesters), service providers, andmiddle agents. The client specifies what they wantfrom services. Client will need to avoid hard codedknowledge of the syntax for interacting withproviders. It is to mediate to classes of functionalitysimilar providers by using information frompublished semantic service description. Three mainfunctions related to composition of semantic webservices in this method are service discovery,engagement and enactment [26].

Service Discovery: In this process agent(clients) identifies required sevices to achive itsobjectives. There are three steps in the process :service providers who perform the services,services requesters seek services that canaccomplished an internal objective, andmatchmaker performs matching between clientrequirement and available services from it its

providers. In the semantic web, queries areprocessed by matchmakers to find appropiateservices among these advertised using semanticweb language descriptions. Abstract capabilitydescriptions in matchmaking queries is moreeffective in trade off mechanism. Clients donegotiation and filtering, in with discovery whichinvolves catched revised capabilityadvertisements.

Service Engagement: Negotiation andcontracts- both the agents (service provider andrequester) are engaged on the agreement on someterms and coditions and compromised values. Thefunctional requirment depends on the interactioncapability, in general and in particular on servicerequest formulation, contract prelimitriaries andcontract negotiation. The negotiation affect thearchitetures requirements such as : negotiationprotocols and and services and auditing services.The service engagement protocols describemessages exchange between providers andrequesters requesting the agreements. Theequivalent protocols are implemented in FIPA. Thetemporal flow and semantic understanding ofnegotiations that can occur between two agents areincorporated in the negotiations committmentprotocol.

Enactment: After engagement the entactmentis fact to be in contact and deal on agreement inspace and time (temporal) continum. The functionsare response interpretation, translationinterpretation, execuation, process mediation anddelegation and dynamic service composition. Themiddle agent has to follow certain protocols andontologies for interacting with user agent as :process mediation services , process schedulingand composition services. The interoperativitywould be enhanced by the adaptive componentsbuilt up by sharing of abstract models rather thansyntactic and abstract agreements amongdevelopers.

Behavior (BDI) Agent

In the Semantic web, the web pages are markedup in accordance with standardized conventions inorder to reduce ambiguity and facilitate automatedreasoning. Web Services is not simply informationbut behavior which can be viewed as thefundamental attribute of intelligence. An agentshould have the following attributes as goals,

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intentions, beliefs, and behavior. Behaviors are theactions the agent is able to take. The processmodel in DAML-S specifies the behavior of theservice [27]. Behavior oriented design is one of theapproaches to model agent behavior consisting ofaction, perception and learning. The action of anagent is supported by the perception composed ofknowledge and beliefs to determine how and whenaction should be expressed. The single behaviormodule may be determined with actions dependenton state and sensing action that mention theaccuracy of the state. The BOD modules areincorporated into hierarchical reactive plans intothe system execution. It includes a specification forparallel rooted ordered slip–stack Hierarchical(POSH) reactive plans. The BOD modules containencapsulated state provide the overall agent withperception primitives. BOD has been implementedin DAML-S.

2.5. Miscellaneous

In this section, we will consider examples forsome other used approaches of semantic webservice composition. We will consider cases forcontext based, iterative, BPEL4WS, templatebased, and logic based approaches for servicecomposition.

Context Based

A composition method based on the context ofuse of semantic web services has been developed[28]. This method considers context of thecomposition as a base for composition. In thismethod the different web services involved areidentified as: composite, web and instanceservices. This system has been implemented onthe top of an open and extensible workbench namelyEclipse. The Eclipse PDE (Plug-in DevelopmentEnvironment) allows the developed plug-ins to beintegrated with the system. So this system consistsof mainly four plug-ins viz. User Interface, WS-execution platform, ontology Repository, and helprepository. The composition process in the systemconsists of firstly consolidating the context ofservices, conciliating contexts, relating services tocontexts, securing contexts, consolidating securitycontexts and then conciliating security contexts.The system defines new language OWL-C fordefining of the context of services. The contextontologies in the system are defined and processed

by using JENA and the OWL-S based ontologiesare processed using Mindswap’s OWL-S APIs. Theuser interface of the system is graphical & userfriendly and is implemented with the help of somecommercially available toolkits like JFACE andSWT (Standard Widget Toolkit).

Iterative Approach

A framework, MoSCoE (Modeling ServiceComposition and Execution) [29], based on iterativeapproach for service composition has beenproposed, in which the incomplete goal servicespecifications are reformulated iteratively forcomposition until the desired goal service isobtained. Services in this framework are modeledusing Symbolic Transition System (STS). So in thissystem, all the component services and goalservices are modeled as STS. The system startswith a choreographer and an abstract STS of goalservice. Then using given component services, asubset is identified and composed withchoreagrapher to create goal service. However inthe case of failure to realize goal service, it modifythe some specific states and transitions of goalservice STS and thus reformulate the goalspecifiction iteratively until a successfulcomposition is obtained. This framework alsoprovide the user with ability to abort the processwhen required.

BPEL4WS based

A semantic web composition model based onBPEL4WS [30] (Business Process ExecutionLanguage for Web Services) has been proposedwhich describes web services as businessprocesses [15]. Model provides a notation forproviding interaction among the web services. Thedifferent service roles in the process model aretreated as partners for providing the integration.The model [31] works on a bottom-up approach. Itfirstly collects all the DAML-S or OWL-S serviceprofiles into a repository. Then the desiredproperties of partner services are defined. Thesemantics of the services in repository areexploited by querying for the partners based on thedescriptions of the partners properties defined.Then semantic service descriptions are integratedby querying into BPWS4J engine (An engine whichconsumes BPEL4WS and WSDL documentsdefining bindings for the BPEL4WS process and its

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partners and implement some features defined inBPEL4WS specification).

Template Based

A semantic web service composition approachbased on the Semantic Process Templates (SPT)has been proposed which uses these templates forcapturing the semantic requirements of theprocesses and thus allowing dynamically changingthe services and partners [32]. SPT uses ontologiesfor describing the activities requirements in thetemplates and thus provide good method ofdescribing and locating the services. Theontologies are build and processed using Jenatoolkit and the composition of the semantic webprocess templates are done using a METEOR-SWeb Service composition Framework tool i.e.Process Builder. Generic process templates arecreated based on the list of activities and controlflow constructs to link the activities. A WSDL (WebService Description Language) editor is used bythe framework for describing the desired processesin WSDL. The activities are then added to theprocess templates opened or created by theprocess designer tool. Also, if needed, the controlflow constructs may also be added to the templates.However activities may also be annotated. Now foreach of the activity some of the services arediscovered, ranked and selected based on somediscovery, matching and Quality of Service (QoS)criteria. Data flow is maintained between theselected services and using a process generatortool which uses WSDL description of processes,process templates and WSDL description ofexisting participating service, the executableprocesses are generated. These executableprocesses can then be deployed and invoked afterthe validation.

Logic Based

A Java based automatic service compositionarchitecture based on Linear Logic (LL) TheoremProving and having graphical user interface hasbeen introduced [33]. The composite services inthe system are represented using Process Calculuswhich is attached to the Linear Logic inferencerules. In the architecture, the services are internallypresented using ExtraLogical axioms and LinearLogic proofs while externally they are presentedusing DAML-S. All the three, DAML-S translator,

Linear Logic theorem Prover and SemanticReasoner operates together for achieving the goal.The system have a translator (transformationbetween internal and external presentation of webservices), Linear Logic Theorem Prover (checkwhether user’s request for service can beachieved), semantic reasoner (detectingrelationship between concepts), and an Adapter(transformation between Linear Logic andDescription Logic [34]). For composing the webservices, the user’s request for composite serviceas well as description of existing web services aretranslated into sequent and extralogical axioms ofLL respectively. Then adaptor requests semanticreasoner to analyze subtype relations of classesand properties and covert them into LL axiom forms,which are send to the LL theorem prover. The LLtheorem prover generates the process calculus ofcomposite service after checking whetherrequested service can be generated or not, whichfurther is converted to DAML-S service model orBPEL4WS.

A multilevel Composability Model

Composibility is checked through a set of rulesorganized in four levels: Syntactic, Static semantic,dynamic semantic and qualitative levels [35]. Theweb services operations (concepts) are defined bya meta-data ontology which provides concepts thatare the description of other conceptions. Theconcepts (services) are defined by a set offunctional and non-functional attributes. Thefunctional attributes are syntactic, static semanticand dynamic semantic. The non functional orqualitative attributes are time availability and cost.The syntactic attributes represent the structure of aservice operation such as input and outputparameters that define operation’s messages. Themeaning of operations or its messages is thesemantic attributes which are of two kinds: staticand dynamic.

Horizontal and vertical composition: Horizontalcomposition is like a supply chain combination ofoperations. For example, a translation of Germanto Hindi can be performed by horizontal compositionof translation from German to English and thenfrom English to Hindi. Vertical composition refersto the subcontracting a services to anotherservices. X has a service to perform but it gives itY to perform and Y returns it to X after performing

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the services. Composability degree refers to theweight assigned to the syntactic and semanticcomposability. The weight signifies the importanceof a level or rule. A minimum threshold value ofcomposability (0 < τ < 1) designates the servicesfor composability.

Semantic Description of Services: Staticsemantics gives various attributes at lowest levelof granularity like serviceability, provider andconsumer types, category and purpose of differentoperations. Static semantics for messages consistof the set of attributes to model the semantics ofmessage parameters like data type, business role,unit and language. Dynamic semantics or businesslogic of an operation refers to the output expectedafter execution of an operation, at the given specificcondition. The business logic is defined by a set ofrules where each rule has the format

(Pre Condition Slkm, Pre Condition ikm)Rm = ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

(Post Parameters Slkm, Post Condition ikm)

Where Pre Parameters ikm and Post Parametersikm are sets of parameters, each parameter isdefined by name, data type, business rule, unit andlanguage of different services (a kind of servicecomposition). It is the discretion of the user tocombine the services from the sensor (at differentphysical location), or perform the pre-processingsteps and deploy the pattern recognition services(statistical or syntactic).

Interactive Composition Techniques

A goal oriented, interactive compositionapproach, driven by filtering and selection ofservices has been developed [36]. This is based onincremental generation of composition by forwardand backward chaining of services. At eachincremented step, new services are added andmatched to current services requirement, enablingthe filtering of undesired services based on theuser’s decision and context of the required serviceprofile.

• After filtering the compatibility of the services,these are delivered as the next step ofcomposition process.

• Propinquity is determined to assess thedesirable composition.

• The input, output, precondition and effect[IOPE] of a service component enhance thefiltering process.

The semantic annotations of services enableservice composition. Two web services onelanguage (German to English) translator andanother dictionary (English to German) can becomposed together to a user’s requirement ofGerman Dictionary. Similarly, another example forservice composition is of sensor data acquisition,sensor data processing.

Semantic Service Description: It is describedin OWL-S language with three components. Serviceprofile which describes the services to beperformed by specifying input and output types,preconditions and effects (IOPE). It is analogous toyellow page like advertisements in UDDI. Theprocess model describes the working of a serviceeither as an atomic process that executes directlyor composite process that is composition of subprocesses. It is similar to business process modelin BPEL4WS.

• The grounding refers to the access andcommunication protocol of an agent;parameters to use in the protocol, and theserialization technique in the communication.It is mapping from OWL-S to WSDL.

• All of the OWL’s domain modeling featurescan be used to directly structure the OWL-Sservice descriptions as well as the conceptsfrom other ontology.

The OWL-S ontology may consist of top levelclass sensor and subclasses as Acoustic sensor,infrared sensor, and microwave sensor. Serviceprofiles are associated with the root class i.e.sensor profile and subclasses with an AcousticSensor profile etc. The extensible serviceparameter can be used to define non IOPEattributes by more specific named classes of“Service profiles” such as “Nearby acousticsensors”. The user can also define complex classexpressions to define their requirements. The“Grounding” contains the information for executingservices like pointers into WSDL description odirectly invoke.

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TABLE 1: Tabular Comparison of Various service composition methods

Name of Composition Process Reasoners Languages Interface SpecificMethod Involved Involved Software

Used

AI Planning Based

Automatic • Convert the available message into Jena Java, BPEL Graphical Oracle

Service required format for BPEL process. User BPM

composition • Select the best one candidate element, interface Engine

[1] if multiple elements are there in available

message.

• Generate executable control flow of

processes by considering the structure of

input & output messages and precondition

& effects of operations.

WS-GEN [2] • Describe web services in State Transition No Specific OWL-S, Graphical MBP tool

system form. Reasoner BPEL4WS user [4]

• Generate plan to control the interaction with interface

existing services.

• Compose services according to goal service.

• Translate the resulting state transition

system goal service into executable

process.

Functionality • Computes casual links between web No Specific OWL-S No specific Ra4C

based services using CLM. Reasoner inteface Algorithm

semantic • Store all casual links. tool

web service • Create semantically ordered and linked

composition plan of web services.

[5]

Workflow based

OWL-S • Construct workflows using atomic, simple or Using OWL-S No specificWSDL2DA

Process composite processes. implemented inteface MLS tool.

Model [6] • A Hierarchy of workflows defines composite process

process. model itself

Knowledge • It works as a composition advice based on Semantic DAML-S WCI GUI Software

Based two models: Service description and based search provides facilitates agent to

Systems [7] conceptual links between services and engine is basic work flow monitor to

their properties. The KBS approach realized schema construction. service

involves identification of knowledge through forContd.....

3. TABULAR COMPARISON

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intensive tasks. The domain level elicitation reasoner describing composi- process

or automatic acquisition of KW using KW FaCT or a web tion and collect

acquisition method. match service. snapshots

• The representation or modeling of domain maker of states

KW in formal, symbolic structure computing when

form. advice is

• Use of KW format user’s requirement to requested

update and maintain formalized KW.

• KBS framework consists of set of components:

Work flow construction environment, a set of

diverse web services, knowledge bases and

ontologies.

Workflow • Find all services with input matching with No Specific No specific Graphical No specific

based requested input. Reasoner Language User tool

service • Add services in Workflow with inputs matching Interface

composition with outputs of last service in workflow.

[8] • Continue until output matches with the

requested output or no unused service.

OntoMat- • Present the web services having WSDL- No Specific WSDL, Graphical No specific

Service represented descriptions before the user Reasoner HTML, user- tool

System [9] using deep annotations. XHTML interface

• Selection of service operations and mapping

rules are done by user.

• Use the selections to compute web service

workflows.

Ontology Based

Framework • Design services graphically. Using OWL-S Graphical ODE SWS

based on • Create instances of framework ontologies. WebODE User [12,13]

stack of • Check the design of service ontologies. Interface

ontologies • Translate the instances into OWL-S specification.

[11]

Composition • Dynamically compose by re-commanding No Specific WSMO Graphical A special

of WSMO- goals according to the context at each step Reasoner ontology, User software

based SWS of composition. Java Interface tool [17]

in IRS-III • Perform orchestration on the generated

[14] compositions.

Integration • Map OWL-S services to WSMO. No Specific OWL-S, Graphical No specific

of OWL-S • Translate WSMO services into OCML. Reasoner WSMO User tool

into IRS-III • Apply IRS-III. ontology, Interface

[15] OCML

Contd.....

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Miscellaneous

ConWe • Consolidate the context. JENA OWL-S, Graphical JFACE,

Scproto • Conciliate contexts. OWL-C user SWL, and

type [28] – • Relate services to contexts. interface indswap’s

Context • Secure contexts. using OWL-S

based • Consolidate security contexts. commercial APIs.

• Conciliate security contexts. toolkits

MoSCoE • Identify a subset of given component No Specific No specific Graphical STS

[29]- services. Reasoner Language User (Symbolic

Iterative • Compose subset with a choreographer to Interface Transition

approach get goal service. System).

• Reformulate goal specification iteratively

until successful composition.

Adapting • Collect DAML-S or OWL-S service profiles No Specific DAML-S, No specific BPWS4J

BPEL4WS into a repository. Reasoner OWL-S inteface Engine

[15] – • Define properties of partner services.

Using • Query for the partners from services in

BPEL4WS repository.

• Integrate semantic service descriptions by

querying into BPWS4J engine.

SPT based • Create Generic Process templates based Jena WSDL No specific Meteor-S

composition on list of activities. inteface tool,

[32]- • Describe desired processes in WSDL. WSDL

Template • Add activities and control flow constructs Editor.

based to the templates.

• Discover, rank and select the services for

the activities based on some criteria.

• Maintain data flow between the selected

services.

• Generate executable processes.

Logic based • Translate the user’s request composite Using a Java, Graphical DAML-S

service services into sequent of LL. specified DAML-S, user translator,

composition • Translate the description of existing web semantic BPEL4WS interface LL theorem

[33] services into extralogical axioms of LL. reasoner prover.

• Analyze subtype relation of classes and module

properties and convert them into LL axioms. in the

• Send the LL axioms such generated to LL system.

theorem prover.

• Generate process calculus of composite

service.

· • Convert it to DAML-S model or BPEL4WS.

Contd.....

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Multi Level • The different levels perform various No Specific WSDL GUI and No specific

composibilty functions on the basis of rules well Reasoner Web DG tool

[35] organized in each level. manager

• Horizontal and vertical composition. using

• Weightage to different types of composability HTML

levels by level weight vector and rule weight servlet

matrix.

• Operations and messages are static and for

dynamic semantics.

Interactive • Filtering of undesired services based on No Specific OWL-S, Graphical No specific

composition user’s decision. Reasoner WSDL User tool

[36] • Determination of propinquity to assess Interface

composition.

• Use IOPE to enhance filtering process.

• Service composition using semantic

annotations.

TABLE 2: Tabular comparison of agent based service composition methods

Name of Composition Reasoners Languages Interface Agent SpecificMethod Process Involved Involved Activity Software

Used

MAS • Input available web services. Using OWL-S Graphical Agent Cooperative

System [20] • Transform them into Linear domain User Cooperation Problem

Logic Formulae. ontologies Interface Solving

• Apply Partial Deduction to in partial Framework.

get requested solution. deduction

process and

agents for

distributed

symbolic

reasoning.

Composition • Generate new DAML-S JTP [22] DAML-S No Agent JADE [23]

Engine [22] specification of services specific Communi-

using Definer module. inteface cation

• Maintain communication

with other agents using

Communication module.

Agent based • Composed by dynamic No Specific DAML-S No Agent No specific

dynamic coalitionof service providing Reasoner specific Communi- tool

service agents (SPA) in the partici- inteface cation,

composition pating task. Negotiation,

Contd.....

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[25] • Decomposes the incoming assessment,

task on the basis of their cooperation.

local knowledge/ontology.

Interactive • Using service discovery, No Specific No specific Graphical Agent No specific

agent [26] agent identifies required Reasoner Language User trust, tool

services to achieve its Interface negotiation

objective.

• Apply service engagement

to engage agents on

agreements on some terms.

• Apply service enactment to

function dynamic service

composition finally.

Behavior • Markup web pages in Using DAML-S No Agent No specific

(BDI) Agent accordance with standardized marking up specific belief, tool

[27] conventions. of the web inteface behavior,

• Specify behavior of service. pages using intentions

• Model agent behavior using standardized

behavior oriented design. conventions

Fig 1 %age of different language-usages

4. OBSERVATIONS

Tables 1-2 are used for generating theobservations on languages, reasoners andinterfaces as described here. Figures 1-3 presentsour observations on usage of various semantic web

languages, implementation languages, reasonersand interface. From Fig 1, observations onlanguages involved in the composition methodssays that, OWL-S is the maximally supported (9 outof 22) semantic web language among thecomposition techniques, DAML-S is the second

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Fig 2 %age of different reasoners- usages

Fig 3 %age of type of user-interface provided

popular (6 out of 22) in support, however WSDL (4out of 22), BPEL (3 out of 22) also have averageresponse in term of support by compositionmethods. Java and related tools (3 out of 22) is themostly used language used for implementation ofmethods. Figure 2, shows that, maximum ofsystems have reasoning capability implementedwithin them (4 out of 22), Jena is the maximallyused (3 out of 22) reasoner, and FaCT, WebODE,and JTP is also used for reasoning on average (1out of 22 each). It is observed, as in Fig 3, mostlyall of service composition systems (12 out of 22)have graphical interface, so they aims at providingease of use; however some of them are using somecommercial toolkits in interface (2 out of 22). Amongthe agent based systems, it has been observed thatmost of the agents are performing communicationand cooperation activities for performing the servicecomposition.

5. CONCLUSION

This next generation of web i.e. semantic webis moving towards its materialization. In the paper,we review some of the popular semantic webservice composition methods. Different categorieslike AI planning based, workflow based, ontologybased, agent based, template based, context basedetc. are observed. They are compared in a tabularform depending upon their different parameters andtaken observations shows that Jena is themaximally used reasoner, and OWL-S and DAML-Sare the maximally used semantic web languages incomposition. Java and related tools is today apreferred tool for implementing the systems andsystems are provided with good ease of use havinggraphical interface.

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AuthorsSandeep Kumar obtained BE in information technology from Haryana University with Gold medal. Presently he is aresearch scholar in Computer Engineering Department, IT, BHU, Varanasi. His research interest is in the area ofsemantic web.

Address: Department of Computer Engineering, Institute of Technology, Banaras Hindu University, (IT-BHU),Varanasi 221 005, India.

email:[email protected]

* * *

R B Mishra is a reader in Computer Engineering Department, IT, BHU, Varanasi. He obtainedBSc (Engg), MTech, PhD He has over 28 years of teaching experience and has publishedaround 80 research papers and articles.

Address: Department of Computer Engineering, Institute of Technology, Banaras HinduUniversity, (IT-BHU), Varanasi 221 005, India.

email: [email protected]


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Corner Detection Algorithms for DigitalImages in Last Three Decades

AMBAR DUTTA, AVIJIT KAR AND B N CHATTERJI

ABSTRACT

Corner detection is an important step in many computer vision applications. A large number of corner detectionalgorithms were already developed. An exhaustive review of the existing corner detection algorithms is, therefore,invaluable for the researchers working in this area. In the present literature, we have found a few reviews on this area.Most reviewers classified corner detectors into two categories – boundary-based and intensity-based. Some reviewersclassified the algorithms into two groups – template-based and geometry-based. Though we follow the firstclassification, still we feel that there is a requirement of further subdivision. So, we subdivide each category intofurther two subcategories – spatial-domain and transform-domain. In this paper, we have reviewed a total of 114corner detection algorithms developed from 1977 to 2006, classified them in each of the four categories and foundthat most of the works have been done in spatial domain only as compared to transform domain approaches.

1. INTRODUCTION

Corner detection in images is an important stepin many tasks in machine vision, including sceneanalysis, motion and structure from motionanalysis, image registration, image matching, objectrecognition etc. A corner is an image point with highcontrast along all the directions. Hence, it is welldistinguished from neighboring points. A cornerdetection algorithm must satisfy the followingcriteria to be useful for feature point matching:(i) consistency, (ii) accuracy, and (iii) speed. Theperformance of corner detection algorithms isaffected by the attributes, viz., corner angle, cornerarm length, corner adjacency, corner sharpness,gray level distribution and noise level.

Vision researchers have proposed aconsiderable number of corner detectionalgorithms. A Rosenfeld et al [1], A Heyden et al[2], Z Zheng et al [3], C Schmid et al [4], P IRockett [5] and F Mokhtarian et al [6, 7] providedgood literature survey on the existing cornerdetection algorithms. Corner detection algorithmscan be broadly divided into two classes – boundarybased and intensity based. Boundary basedmethods rely on extraction of contours of regionseither by edge detection or by segmenting theimage into regions followed by boundary finding.These methods, thus, largely depend on asegmentation process. On the other hand, the

intensity based methods estimate a measure todetect corners directly from the gray values of theoriginal images without a prior segmentation. Eachof these two above categories can further besubdivided into spatial domain and frequencydomain methods. Spatial domain methods directlyoperate on the pixel values of the image. Intransform domain methods, the image is firsttransformed to some other domain, which is thenpassed through a suitable filter and finally thefiltered information is mapped back to the spatialdomain with the help of an inverse transformoperation.

The remainder of the paper is structured asfollows. In section 2, we discuss differentperformance measures of corner detectors. In thesubsequent sections (section 3-6), we present aliterature survey of 114 corner detection algorithmsunder different categories of corner detectionalgorithms. Finally, we conclude in section 7.

2. PERFORMANCE MEASURES FORCORNER DETECTORS

The exact number of corners in the image, thenumber of corners truly detected, the number ofcorners missed and the number of extra cornersdetected play very important role for thecomparison of corner detection algorithms. Toevaluate the performance of corner detection

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Spatial Domain Transform Domain Spatial Domain Transform Domain

Corner Detection Algorithms

Boundary based Intensity based

Fig 1 Classification of corner detection algorithms

algorithms, a few performance measures hadalready been proposed in the literature [6, 7, 108].We have also proposed three performancemeasures – Detection Gradient, False PositiveRatio and False Negative Ratio, comparable withthe existing measures, which are defined below.

| NA – ND | + | NM + NF |DG = ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

NA

NFFPR = ⎯⎯⎯

NA

NMFNR = ⎯⎯⎯

NA

where NA, ND, NM and NF denote the total numberof corners present in the image, the number of truecorners detected, the number of missed (falsenegative) corners and number of extraneous (falsepositive) corners detected in the image,respectively. In the best case, the value of DG iszero, which signifies that all the corners arecorrectly detected without any missed or falsecorners; the same is true for FPR and FNR.

3. BOUNDARY BASED SPATIALDOMAIN METHODS

Freeman and Davis [8] and Rutkowski andRosenfeld [1] detected corners by using chain-code values. Medioni and Yasumoto [9] used B-

Splines to develop a technique for corner detectionand curve representation. Beus and Tiu [10]developed an algorithm based on chain-coded planecurves that eliminates the detection of spuriouscorners using a maximum cut-off value to determinethe length of the forward and backward arms ofeach point. Davies [11] proposed corner detectorbased on generalized Hough transform. Ogowa [12]computed a symmetry measure at every point on adigital curve and then extracted corners at the localmaxima of the measure. Rangarajan, Shah andBrackle [13] proposed an optimal gray level cornerdetector based on Canny’s optimal one-dimensionaledge detector [14]. Rattarangsi and Chin [15]presented scale-based corner detection algorithmon planar curves. Mehrotra, Nichani andRanganathan [16] described two methods for cornerdetection, one was based on the first directionalderivative of Gaussian and the other was based onthe second directional derivative of Gaussian. Thedetector also computed corner angle andorientation. Bell and Pau [17] developed a cornerdetector based on Freeman’s chain-code. Liu andTsai [18] proposed a method based on the principleof preserving gray and mass moments. Cooper,Venkatesh and Kitchen [19] used two differentapproaches to detect corners in an image – one, byusing dissimilarity along the contour direction todetect curves in the image contour, and the otherby estimating image curvature along contourdirection. Orange and Greon [20] proposedsegmentation model for the detection of corners

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based on a geometrical model. Xie, Sudhakar andZhuang [21] presented a cost minimizationapproach to corner detection in which theyassociated a corner with different cost factorscapturing desirable characteristics of a corner. Peiand Horng [22] extracted corners from curvaturelocal maxima of the shape resulting from the nestmoving average of the original image. Seeger andSeeger [23] detected corners from a gray-levelimage by a real-time parameter-free algorithm.Sugimoto and Tomita [24] proposed an algorithmfor the detection of various feature points (corner,inflection and transition). Zhang, Haralick andRamesh [25] described a maximum a posteriori(MAP) probability technique for corner detection.Pikaz and Dinstein [26] proposed an approachbased on a simple decomposition of the curve intothe minimal number of concave and convexsections to detect feature points and to smoothnoisy digital curves. Sohn, Alexander, Kim andSnyder [27] presented a constraint regularizationapproach to detect corners that overcome theproblem of determining the unique smoothing factor.Koplowitz and Plante [28] proposed a scheme forchain-coded curves by measuring the number oflinks to either side of a point that can produce thelargest digital straight line. Ray and Ray [29]presented a corner detection algorithm using aniterative Gaussian convolution with constantwindow size. Dias, Kassim and Srinivasan [30]presented an artificial neural network based cornerdetection algorithm in a 2-D image. Wang, Wu,Huang and Wang [31] proposed a modified contourtracking and efficient corner detection algorithmusing bending value. Lai, Paul and Wu [32]presented an edge-corner detection algorithm,called Cellular Vectorization Method. Sheu and Hu[33] proposed a rotationally invariant two-phasescheme for corner detection where the candidatecorners are detected first and then these cornersare reinvestigated for the global trend. Arrebola,Camacho, Bandera and Sandoval developedtechniques which are based on local [34] andcircular [35] histograms of the contour chain code.Ji and Haralick [36] applied statistical techniquesto detect corners from chain-encoded digital arcs.Ray and Ray [37] used a discrete scale-spacekernel to detect corners on a digital arc. Tsai [38]proposed a boundary based corner detectionalgorithm by developing two artificial neuralnetworks, one for detecting corners with highcurvature, and the other for detecting points of

tangency and inflection. Zheng and Zhao [39]implemented a parallel algorithm for detectingdominant points on multiple digital curves. Shilat,Werman and Gdalyahu [40] presented a method forthe detection of corners along ridges/troughs andlocal minima points. Mokhtarian and Soumela [41]presented a corner detection algorithm basedcurvature scale space representation in which firstthe edges are extracted from the original imageusing Canny edge detector [14], and then cornersare found from the edge image where there is alocal maxima of absolute curvature. Sohn, Kim andAlexander [42] presented a mean field annealingapproach to boundary smoothing for curvatureestimation to improve the capability of detectingcorners. Li and Chen [43] described a cornerdetection algorithm on planar curves as a fuzzyclassification problem containing three stages –evaluation, classification and location. Luo, Crossand Hancock [44] described a corner detectionalgorithm based on the topographic analysis of avector potential image representation. Tsai, Houand Su [45] presented a quantitative measure ofcorners based on the smaller eigen value of thecovariance matrix of boundary points over a smallregion of support. Guest and Fairhurst [46]described a clustering approach to corner pointanalysis in hand-drawn images. Ludtke, Luo,Hancock and Wilson [47] proposed a cornerdetection algorithm using mixture model of edgeorientation. Oh and Chien [48] described a cornerdetection algorithm by combining the generalizedsymmetry transform (GST) operator with theparametric corner equation. Shen and Wang [49]proposed a fast corner detector using modifiedHough transform. Ray and Pandyan [50] presentedan adaptive corner detector for planar curves. Marjiand Siy [51] proposed an algorithm for detectingdominant points and polygonal approximation ofdigitized closed curves. Wu [52] proposed anefficient method for dominant point detection usingadaptive bending value. Urdiales, Trazegnies,Bandera and Sandoval [53] presented a fastalgorithm for corner detection defined at differentscales by estimating the curvature of a contour in alocal adaptive way. Banerjee, Kundu and Mitra [54]presented a support vector machine basedalgorithm for corner detection. Guru, Dinesh andNagabhushan [55] presented a fast and efficientcorner detection algorithm by introducing a newmeasure “cornerity index” for the quantification ofthe prominence of a corner point. Arrebola and

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Sandoval [56] detected corners by means of thehierarchical computation of a multi-resolutionstructure. Sarfraj, Rasheed and Muzaffer [57]presented a simple, robust and efficient real-timecorner detection algorithm based on the change ofsign of slope of the curve along the contour. Olagueand Hernandez [58] proposed an accurate andflexible model-based multi-corner detector. Poyato,Garcia, Carnicer and Cuevas [59] described anefficient algorithm for detecting dominant points onthe boundaries of digital planar curves. Sobaniaand Evans [60] proposed a morphological cornerdetector using paired triangular structuringelement. The detector operated on the boundariesof a segmented image in a binary format anddetected only interior or concave corners.Mokhtarian and Mohanna [7] presented a niceliterature survey on the existing corner detectionalgorithms, provided two performance measure,accuracy and consistency, for corner detectionalgorithms and gave an enhanced version of originalCSS corner detection algorithm [41], which workedon multiple scales also.

4. BOUNDARY BASED TRANSFORMDOMAIN METHODS

Chen, Lee and Sun [61-63] proposed multi-scale gray-level corner detection algorithms basedon the modulus and orientation information of thewavelet transform which are used to detect edgesand localize corners respectively. They alsoprovided a multi-scale corner detection algorithmbased on the wavelet transform of contourorientation. Kohlmann [64] derived a featuredetection algorithm with 2-D intensity changesusing 2-D Hilbert transform. Quddus and Falmy[65, 66] proposed a wavelet-based corner detectionalgorithm on planar curves. Peng, Zhou and Ding[67] proposed a boundary-based corner detectionusing wavelet transform. Gao, Sattar, Quddus andVenkateswarlu [68] proposed a multi-scale cornerdetection algorithm based on continuous wavelettransform on contour images. Yeh [69] proposed arobust, rotation- and scale-invariant wavelet-basedcorner detection algorithm on circular arcs by usingthe eigen vectors of the covariance matrices. Sun,Tang and You [70] proposed a wavelet-based cornerdetection algorithm by estimating the curvature of acontour in an adaptive way.

5. INTENSITY BASED SPATIALDOMAIN METHODS

Moravec [71] gave the concept of “points ofinterest” as points where a significantly highintensity variation occurs in every direction.Beaudet [72] presented a determinant operator,which has large values near the corners. Kitchenand Rosenfeld [73] were the first to apply thedifferential operators that consists of first andsecond order partial derivatives of the image todetect corners. They proposed a cornernessmeasure based on the product of the gradientmagnitude and the change of the gradient directionalong an edge contour. The local maxima of themeasure isolated corner points. The detector isvery sensitive to noise. Wu and Rosenfeld [74]detected corner points by a filter projection method.Zuniga and Haralick [75] proposed a facet modelbased corner detector. Paler, Foglein, Illingworthand Kittler [76] extracted corners from the localdistribution of gray level values. Harris andStephens [77] estimated the measure of localautocorrelation using first-order derivatives that issuggested by performing an analytic expansion ofthe Moravec [71] operator. At each pixel location a2X2 autocorrelation matrix is computed and if boththe eigen values are large, the pixel is consideredto be a corner. The algorithm is computationallyexpensive. Forstner and Gulch [78] used the samecornerness measure as Harris and Stephens [76],but their algorithm has higher computationalcomplexity. Tomasi and Kanade [79] derived thesame equation by analyzing the optical flowequation presented by Lucas and Kanade [80].Noble [81] explained the method of estimation ofimage curvature by Harris and characterized two-dimensional surface features. Deriche andGiraudon [82] detected corners using a scale spacebased approach by combining useful propertiesfrom Laplacian and Beaudet’s cornerness measure.Shi and Tomasi [83] proposed a method for featureselection, a tracking algorithm based on a model ofaffine image changes, and a technique formonitoring features during tracking. Wang andBrady [84, 85] presented a simple and accuratecorner detector based on the cornernessmeasurement of the total surface curvature. Cuiand Lawrence [86] proposed scale-spaceconsistent algorithm to detect corners in binaryimages based on corner attributes. Lee and Bien[87] formulated a pattern classification problem to

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detect gray-level corners in real-time algorithmusing fuzzy logic. Diaz, Domingo and Ayala [88]detected two-dimensional feature point from agrayscale image using the application of statisticaltechniques. Smith and Brady [89] proposed SUSANcorner detector using a concept that each imagepoint is associated with it a local area of similarbrightness. If the brightness of each pixel within amask is compared with the brightness of thatmask’s nucleus, then an area of the mask can bedefined which has the same (or similar) brightnessas the nucleus. This area of the mask is known asthe “USAN”, an acronym for “Univalue SegmentAssimilating Nucleus”. The value of USAN getssmaller on both sides of an edge and becomes evensmaller on each side of a corner. The local minimaof the USAN map represent corners in the image.Kautsky, Zitova, Flusser and Peters [90, 91]proposed a two-stage method for the detecting thefeature points in which all possible candidates arefound first and then the desirable number ofresulting significant points is selected among themby maximizing the weight function. Laganiere [92]proposed a morphological corner detector using anasymmetric closing operator. Lin, Chu and Hsueh[93] also used mathematical morphologicaloperators for corner detection with simple integercomputation. Stammberger, Michaelis, Reiser andEnglmeier [94] proposed a hierarchical approach tocorner detection in which the whole image isconvolved with only one kernel. Trajkovic andHedley [95] proposed a fast corner detectionalgorithm using a multigrid approach to reduce thecomputational complexity and to improve thequality of the detected corners. Chabat, Yang andHansell [96] used an operator to detect the truelocation and orientation of corners. Lv and Zhou[97] modified the cornerness measure of Kitchen-Rosenfeld detector [73] based on the perception ofhuman vision system to make the detector effectivein variable illumination scenes. Zheng, Wang andToeh [2] presented a gradient direction cornerdetector, derived from the Plessey corner detector,which is based on the measure of the gradientmodule of image gradient direction. Sojka [98]presented a reliable, robust corner detectionalgorithm in digital images. Ruzon and Tomasi [99]used both a region model based on the distributionsof pixel colors and an edge model for the detectionof corners in textured color images. Basak andMahata [100] proposed a connectionist model todetect corners in binary and gray images.

Deschenes and Ziou [101] presented an approachto detect the line junctions in gray images. Alvarez,Cuenca and Mazorra [102] proposed amorphological corner detection algorithm toestimate corners with sub-pixel accuracy andtested the detector’s accuracy in the problem ofmultiple camera calibration. Wurtz and Laurens[103] presented a corner detection algorithm incolor images through a multi-scale combination ofend-stopped cortical cells. Telle and Aldon [104]proposed a interest point detector for color imagesbased on non-linear filtering of the image. Gao, Yu,Sattar and Venkateswarlu [105] proposed animproved Plessey corner detector, worked in scale-space domain, for gray level images using multi-scale analysis. Bae, Kweon and Yoo [106] used twooriented cross operators, COP (crosses as orderedpair) to extract low-level image features, viz.,corners. Elias and Laganiere [107] proposed anapproach based on a data structure similar topyramid, but of circular levels, to determine thelocation and orientation of corners. Golightly andJones [108] presented an algorithm for cornerdetection and matching for visual tracking of powerline inspection and proposed two performancemeasures – detection rate and error rate – for thecorner detection algorithms. Mikolajczyk andSchmid [109] presented two techniques for cornerdetection invariant to scale and affinetransformations. Alkaabi and Deravi [110]presented a fast corner detection algorithm basedon pruning candidate corners. Zhou, Liut and Cai[111] improved SUSAN corner detector [89] bypresenting a robust and efficient corner detectionalgorithm which is capable of detecting the featuresin different contrast images automatically throughself-adjust multi-threshold. Kenney, Zuliani andManjunath [112] presented an axiomatic approachto corner detection. Vincent and Laganiere [113]proposed a new feature point detector which firstperforms a simple segmentation based on theintensity values found in the vicinity of eachconsidered point, and then, it tries to fit a simplewedge corner model to the resulting segmentedarea. Cooke and Whatmough [114] proposed twoways – one by using genetic algorithm and anotherby using supervised classification techniques –towards the application of learning algorithms incorner detection. Pei and Ding [115] proposed acorner detection algorithm based on determiningorientations of the adaptive vertical and tangentaxes and observing the variations of brightness

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along the positive and negative directions of theseaxes. Rosten and Drummond [116] used machinelearning for fast and high quality corner detection.

6. INTENSITY BASED TRANSFORMDOMAIN METHODS

Quddus and Falmy [117] proposed a cornerdetection algorithm based on scale interactionmodel using Gabor filter suitable for both binary andgray level images with variable background.Pedersini, Pozzoli, Sarti and Tubaro [118] proposeda wavelet-based multi-resolution corner detectionalgorithm based on the analysis of multi-scaleLaplacian’s profile. Quddus and Gabbouj [119]proposed a wavelet-based corner detectionalgorithm using singular value decomposition(SVD). Gao, Sattar and Venkateswarlu [120]presented a multi-scale corner detection algorithmon gray images using Gabor wavelets.

7. CONCLUSIONS

In this paper, we provide a literature survey onexisting corner detection algorithms developed inthe last three decades starting from 1977 whichinclude a total of 114 algorithms. We then classifythese algorithms into two main categories –boundary-based and intensity-based methods,which, in turn, are again subdivided into spatialdomain and transform domain methods. After thisclassification, we have observed that out of these114 algorithms, 54 algorithms belong to boundary-based spatial domain, 10 belong to boundary-basedtransform domain, 46 belong to intensity-basedspatial domain and only 4 to intensity-basedtransform domain. But since transform domainmethods are intensely used in various applicationsin image analysis and processing andcomparatively much less work has been carried outin transform domain, there is a tremendous scopeof work in the field of corner detection in transformdomain. Moreover, all transform domain methodsdiscussed in this survey were based on wavelettransform and since there are many othertransforms available, e.g. Fourier, Hartley, Walsh-Hadamard, Haar, Slant, Karhunen-Loeve etc, soefficiency of these transforms may be investigatedand noted. Though the majority of work in this fieldhas been carried out in the spatial domain, stillthere is scope more work in this area. We have also

found that most of the work has been carried out onboundary (binary) and gray-scale images. So, thereis huge scope of work in the field of corner detectionthat will directly operate on the color images aswell. In addition, any boundary-based cornerdetection algorithm consists of finding boundaryfrom an image, followed by following and closingthe boundary, which always requires some manualintervention resulting in high computational time.

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Authors

Ambar Dutta born on 3rd December, 1977, did his BSc (Honors) in Mathematics fromPresidency College, Kolkata in 1999 and MCA from Jadavpur University, Kolkata in 2002. He isworking as a Lecturer in the department of Computer Science and Engineering, Birla Institute ofTechnology, Mesra, Kolkata Extension Centre. He is pursuing his PhD from JadavpurUniversity, Kolkata in the area of image processing (corner detection and matching). Hisresearch interest includes Image Processing, Pattern Recognition, Data Mining and SoftComputing.

Address: Department of Computer Science and Engineering, Birla Institute of Technology,Mesra, Kolkata Extension Centre, Southend Conclave, 1582, Rajdanga Main Road,Kolkata 700 107, India.


* * *

Avijit Kar did his MSc and PhD in 1980 and 1984 respectively from IIT Kharagpur. He iscurrently a professor in the department of Computer Science and Engineering in JadavpurUniversity, Kolkata. He has supervised several PhD theses and is actively involved in many R& D activities and IT related consultancy for Government of India and the private sector. Hisresearch interest includes biomedical imaging as well as SAR imaging. He is also into computersystems reliability. He is involved in a large number of industry sponsored developmentprojects.

Address: Department of Computer Science and Engineering, Jadavpur University, Kolkata700 032, India.


* * *

B N Chatterji born on 10th November, 1942, obtained BTech (Hons) (1965) and PhD (1970) inElectronics and Electrical Communication Engineering of IIT, Kharagpur. He did Post Doctoralwork at University of Erlangen-Nurenberg, Germany during 1972-73. Worked with Telerad PvtLtd, Bombay (1965), Central Electronics Research Institute, Pilani (1966) and IIT, Kharagpur asfaculty member during 1967-2005. He was Professor during 1980-2005, Head of the Departmentduring 1987-1991, Dean Academic Affairs during 1994-1997 and Member of Board of Governorsof IIT, Kharagpur during 1998-2000. He has published about 150 journal papers, 200 conferencepapers and four books. He was Chairman of four International Conferences and ten NationalConferences. He has coordinated 25 short-term courses and was the chief investigator of 24Sponsored Projects. He is the Fellow/Life Member/Member of eight Professional Societies. Hehas received ten National Awards on the basis of his Academic/Research contributions. Hisareas of interests are Pattern Recognition, Image Processing, Signal Processing, ParallelProcessing and Control Systems.

Address: Department of Electronics Communication Engineering, B P Poddar Institute ofManagement and Technology, 137, VIP Road, Kolkata 700 052, India.



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SPIHT: Highly Efficient Technique for ImageTransmission and Coding

NILKANTH B CHOPADE AND A A GHATOL

ABSTRACT

Uncompressed multimedia data (graphics, audio and video) requires considerable storage and transmission bandwidth.A fundamental goal of data compression is to reduce the bit rate for transmission or storage while maintainingacceptable quality. Image coding and compression technique, converts an image that requires low memory storagesspace, smaller bandwidth for transmission, high PSNR with acceptable image quality. This paper presents waveletbased coding algorithm SPIHT to encode and compress an image data. The coding and decoding process iscomparatively fast. The numerical results obtained using MATLAB shows that the output image has high value ofPSNR with good compression ratio for low bit rate.

INTRODUCTION

In many fields, digitized images are replacingconventional analog images such as photographs,X-ray, MRI images. Multimedia data includinggraphics & audio requires large memory space,more transmission bandwidth & large channelcapacity for their transmission. Despite rapidprogress in mass-storage density, processorspeeds, and digital communication systemperformance, demand for data storage capacityand data-transmission bandwidth continues tooutstrip the capabilities of available technologies.The recent growth of data intensive multimedia-based web applications have not only sustained theneed for more efficient ways to encode signals andimages but have made compression of such signalscentral to storage and communication technology[1]. It has been estimated that over 80 billion newdigital images are produced yearly. Compression &coding of these images reduces storage cost,channel bandwidth & transmission rate [2].Common characteristics of most of images are thatthe neighboring pixels are highly correlated.Therefore most important task is to find a lesscorrelated representation of image. Thefundamental components of compression arereduction of redundancy and irrelevancy.Redundancy reduction aims at removing duplicationfrom the image. Irrelevancy reduction omits partsof the signal that will not be noticed by the signalreceiver namely the human visual system (HVS).

Three types of redundancies can be identified:Spatial redundancy, Spectral redundancy andtemporal redundancy. In still image, thecompression is achieved by removing spatialredundancy and Spectral redundancy [3-5].

1. DISCRETE WAVELET TRANSFORM

Wavelets are the functions that satisfy certainmathematical requirements and are used inrepresenting data or other functions. The basic ideaof the wavelet transform is to represent anyarbitrary signal as a superposition of a set of suchwavelets or basis functions. These basis functionsare obtained from a single proto-type wavelet calledthe mother wavelet by dilation (scaling) andtranslation (shifts). Mathematically DWT using two-scale relation can be expressed as equation (1).The two scale relation states that the scalingfunction at a certain scale can be expressed interms of translated scaling functions at the nextsmaller scale [6].

φ (2 j t) = Σ hj+1 (k) φ (2 j+1 t – k) (1)k

1.1. Discrete Wavelet Transform (DWT)Applied to Image

In case of discrete wavelet, the image isdecomposed into a discrete set of waveletcoefficients using an orthogonal set of basis

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functions. These sets are divided into four partssuch as approximation, horizontal details, verticaldetails and diagonal details; these components arecalled as nodes of a tree as shown in Fig 1 [3].

image component & HH component is the result ofhigh pass filtering of rows & columns [5].

2 THE PEAK SIGNAL TO NOISERATIO (PSNR)

The PSNR metric is a well utilized and industryaccepted metric for the objective quantification ofimage compression algorithm performance. ThePSNR is a function of mean square error (MSE).

N – 1 ⎢ ⎢ 2

MSE = Σ ⎢ xi – xi ⎢ (2)i = 0 ⎢ ⎢

⎛ M 2 ⎞PSNR = 10 log10 ⎜ ⎯⎯⎯⎯ ⎟ (3)

⎝ M S E ⎠

Where xi , xi are the input & reconstructed pixelvalues in the image respectively & M is themaximum peak to peak value in the image (typically256 for 8 bit image). A good PSNR performance isa prerequisite for any modern compressionalgorithm [7,8].

3. SPIHT METHOD FOR IMAGE COM-PRESSION

The SPIHT technique is based on a wavelettransform, and differs from conventional waveletcompression in encoding the wavelet coefficients.It uses three principles:

(i) Exploitation of the hierarchical structure ofthe wavelet transform, by using a tree-basedorganization of the coefficients.

(ii) Partial ordering of the transformed coefficientsby magnitude, with the ordering data notexplicitly transmitted but recalculated by thedecoder.

(iii) Ordered bit plane transmission of refinementbits for the coefficient values.

This leads to a compressed bitstream in whichthe most important coefficients (regardless oflocation) are transmitted first, the values of allcoefficients are progressively refined, and therelationship between coefficients representing thesame location at different scales is fully exploitedfor compression efficiency. The partial ordering ofthe transform coefficients is a result of comparisonsof coefficient magnitudes to a set of octavely

Fig 1 Pyramidal structure of 3-level waveletdecomposition

Fig 2 Parent offspring dependencies in tree basedorganization in wavelet transform

^

^

HL1

HH1 LH1

LH2 HH2

HL2 LL3 HL3

LH3 HH3

A 2D DWT of an image is obtained by using Lowpass & High pass filters successively shown inFig 3. An image component obtained by low passfiltering of rows & columns is LL image. Low passfiltering of rows & high pass filtering of columns,gives LH image component. High pass filtering ofrows & low pass filtering of columns gives LH

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Fig 3 Subband decomposition of an image

decreasing thresholds, with the initial thresholdbeing the largest power of 2, which is smaller thanthe magnitude of the largest coefficient. At anytime, coefficients with magnitudes larger than thecurrent threshold are deemed to be significant andothers insignificant. The coefficients are consideredto be organized into trees, as shown in Fig 2.Normally, most of the image energy is concentratedin the low frequency components. Consequently,the variance decreases as we move from thehighest to the lowest levels of the wavelet pyramid.It has been observed that there is a spatial self-similarity between levels, and the coefficients areexpected to be magnitude ordered if we movedownward in the pyramid following the same spatialorientation [7].

This organization will allow us to identify largesubtrees as containing no significant pixels. Thisalgorithm maintains lists of insignificant sets,insignificant pixels, and significant pixels, and isinitialized with the list of insignificant pixels beingthe nodes in the highest layer and the list ofinsignificant sets being the sets of the subtreedescendants of each such node. In a ‘sorting’ pass,the algorithm works its way down the list ofinsignificant pixels first, testing their magnitudeagainst the current threshold, outputting theirsignificance, and, when one is significant,outputting its sign and moving it to the list ofsignificant pixels. Then, it moves through the list ofinsignificant sets, performing the magnitude test to

all the coefficients in the current set (which initiallyare full subtrees of descendants). When all thecoefficients in a set are insignificant, that fact canbe indicated by outputting only one bit. When agiven set contains a significant pixel, it is partitionedinto subsets (subtrees rooted at the descendants ofthe current root) and those in turn are tested forsignificance. As before, when significant pixels areidentified, their sign is transmitted and they aremoved to the list of significant pixels. After a passhas been completed through the lists of insignificantsets and pixels, a ‘refinement’ pass is made throughthe list of significant pixels (not including those justadded in the last sorting pass) and the values ofthose coefficients are refined by one bit. After anentire pass is made, the threshold is decreased bya factor of two and another sorting pass is initiated.

The output bitstream thus consists of the resultof significance tests and signs and refinement bitsof currently significant coefficients. The location ofthe coefficients being refined or classified assignificant is never explicitly transmitted; it isknown implicitly since the encoder and decoderboth share the same algorithm, and since allbranching decisions as the encoder searches thecoefficients are output. Arithmetic coding can beapplied to the output bitstream to further compressthe results of the significance tests, at the expenseof more computation time. The signs and refinementbits both tend to be 0 or 1 with equal probability, sothere is nothing to gain by compressing them.

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Fig 5 PSNR performance for different image coding algorithms

Fig 4 Numerical results for different images using SPIHT

Proceeding in this manner, the algorithmtransmits approximations of the most importantcoefficients (those with largest magnitude) first,and refines the values of all significant coefficientsone bit at a time [1]. This results in a progressive

transmission, which always selects the mostimportant information that yields the largestdistortion reduction to transmit next. If the wavelettransform is unitary, then the Euclidean norm ispreserved and it can be shown that such a

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Fig 6 Results of the SPIHT algorithm for different Images

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TABLE 1: PSNR and compression ratio values for different Images

Image MRI (256X256) & Lena (512X512) & Cameramen (256X256) Rice (256X256) & 8 bit 8 bit &12 bit 12 bit

Bit Rate PSNR Compression PSNR Compression PSNR Compression PSNR Compression(Bits / Sec) (dB) Ratio (%) (dB) Ratio (%) (dB) Ratio (%) (dB) Ratio (%)

0.2 33.81 83.28 32.52 80.22 28.84 90.00 30.00 88.45

0.4 37.23 81.16 35.00 79.56 31.52 88.43 31.83 88.19

0.6 40.00 79.34 36.74 78.34 32.18 85,10 32.58 87.23

0.8 42.62 78.88 37.32 74.80 33.65 81.64 32.90 82.60

1.0 44.94 64.65 39.22 68.94 35.48 77.33 34.81 78.34

transmission scheme is the optimal way todecrease the RMS error in the reconstructed image[6]. The bitstream, which is generated, is fullyembedded, which means that it can be truncated (orthe compression process stopped) at any point andthe image decompressed and reconstructed. Infact a single file compressed at a high bit rate (lowcompression ratio) can be decompressed at anysmaller bit rate (higher compression ratio) and theresults will be identical to having compressed thefile at the higher ratio to begin with. The algorithmachieves fully progressive transmission: at anytime the transmission of another byte resulting insimply further refining the values of one or morecoefficients. Thus, the desired compression ratioor bit rate can be fully specified in advance incontrast to JPEG or to the conventional waveletapproach.

4. NUMERICAL RESULTS

The SPIHT coding/decoding algorithm has beenimplemented in MATLAB. It has been tested onimages of different of size & type. The DWT level iskept equal to 3. The numerical results are presentedin tabular form (Table 1) in terms of PSNR values &Compression ratio for different bit rate. Figure 5shows the comparative performance of theproposed algorithm.

5. CONCLUSION

In this paper wavelet based SPIHT imagecoding algorithm has been presented. The designed

MATLAB codes are tested with images of differentsize & type. From the results it has been found thatincreasing bit rate, PSNR increases & compressionratio decreases. The results of this coding algorithmwith its embedded code and reduced executiontime are so impressive that it can be used forstandardization in many image compressionsystems.

6. REFERENCES

1. A Said & W A Pearlman, Image compression usingthe special orientation tree, IEEE Int Symp Circuitsand System, Chicago, pp 279-282, May 1993.

2. A Said & W A Pearlman, An Image MultiresolutionRepresentation for lossless & lossy Imagecompression, IEEE transaction on image processing,vol 15, pp 1303-1310, Sept 1996.

3. A Manduca & A Said, Wavelet Compression ofmedical images with SPIHT, SPIE symposium onMedical imaging, Cambridge, MA, March 1996.

4. R A Dvorer, B Jawerth & B J Lucier, Imagecompression through wavelet transform coding, IEEETransactions on Information Theory, vol 38, pp 719-746, Mar 1992.

5. P N Topiwala, Wavelet Image & Video Compression,Kluwer Academic publisher 2002.

6. K P Soman & K I Ramchandran, Insight intowavelets, from theory to practice, PHI, New Delhi2004.

7. M Vetterli J Kovaccevic, Wavelets & SubbandCoding, Prentice Hall PTR, Englewood cliffs, NJ.

8. Yong Sun, Hui Zhang & Guangshu Hu, Real-timeimplementation of a new low-memory SPIHT imagecoding algorithm using DSP chip, IEEE Transactionson Image Processing 11(9), pp 1112-1116, 2002.

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AuthorsN B Chopade, received MSc (Applied Electronics) & ME (Electronics) degrees from SGBAmravati University, Amravati in the year 1992 & 1998 respectively. He scored third meritposition in Amravati University in the year 1992. His areas of research are DSP, Imageprocessing, Wavelets Applications. Currently he is pursuing PhD from SGB Amravati University,Amravati. He has joined SSGM College of Engineering, Shegaon in 1992 and is currentlyworking as Selection Grade Lecturer in the Department of Electronics and TelecommunicationEngineering. He is a member of I E (India), IETE, BES (India) and ISTE. He has presentedseveral papers in National, International Conferences / Seminars.

Address: Department of Electronics and Telecommunication Engineering, SSGM College ofEngineering, Shegaon 444 203, India.


* * *

A A Ghatol, is presently Vice-Chancellor of Dr Babasaheb Ambedkar Technological University,Lonere-Raigad (M.S). He is also acting as a Chairman, Western Regional Committee, AICTE,New Delhi. Before joining as Vice-Chancellor, he was Principal/Director at Pune Institute ofEngineering and Technology and Principal at Government College of Engineering, Amaravati.He has completed BE from Nagpur University in year 1971, M Tech and PhD from IIT, Mumbaiin the year 1973 and 1984 respectively. He has been actively involved in the field of TechnicalEducation as Academician, Researcher, Teacher, Planner and Administrator. He is VicePresident of ISTE, Fellow of IETE, IE (India) and Ex-Member of IEEE. He has guided severalME and Ph D students in Electronics Engineering discipline

Address: Dr. Babasaheb Ambedkar Technological University, Lonere, Dist Raigad 402 103,India.


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For Advance Information of AuthorsA New System for

On-Line Submission and Review of Articles for IETE Journals

Subject of on-line submission & review of articles for IETE Journals has been underconsideration for a long time. Keeping the present trend of Internet, automation andcomputerization at different levels, it was considered prudent and necessary to move frommanual hard copy submission and review system to on-line system. The proposed softwareis expected to help authors, Honorary Editors, Reviewers, Editors-in-Chief and IETE HQsto streamline submission and reviewing process for timely publication of journals.

The procurement of software system has been approved by the Council of IETE and willbe operationalized in the near future by end September 2008. Effective date for systemwill be publicised in IETE Journals and website.

The salient features of the proposed system are as follows:-

a) The system will create the website and a separate button to choose the journal fromthe list. All the three journals will appear on the page. The authors will choose one ofthe journal and upload the manuscript. These will be menu driven and simple.

b) Resource templates based on approved standard would be hosted on IETE websitealong with the guidelines to authors for submission of articles on line with domainaddress www.ietejournals.org.

c) The chairpersons of the editorial boards would be the Editors-in-Chief for the threejournals, i.e. IETE Journal of Education (JE), IETE Journal of Research (JR) and IETETechnical Review (TR).

d) Each journal will have area editors from across the world as empanelled by therespective editor-in-chief. These panels would be periodically reviewed taking intoconsideration the performance of the panelists.

e) The concerned Editor-in-Chief will carry out on-line preliminary review for furtherprocessing or out-right rejection with intimation to author. The IETE HQ will keep therecord of entire submission, revision, rejection and acceptance details.

f) After final acceptance by the Editor-in-Chief, an e-mail will be generated automaticallyto the Administrator, Publication Section at IETE HQs who will have the access to theword / pdf document of the final accepted manuscript. The administrator will downloadit and carry out all editorial changes before it is given to Press for printing of the journals.

h) Authors are requested not to submit any articles to IETE, neither through e-mailnor by post, till the on-line system is available, which is expected by endSeptember 2008.

g) The printed journals will be hosted on IETE website for downloading by readers / authorsand will be available on IETE website.

Any suggestions and feedback are most welcome. Please email to [email protected]

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IETE-May-June (2008) Main File

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