CACI Private Data; Do Not Copy or Distribute Information Fusion Technical Area Overview &...

CACI Private Data; Do Not Copy or Distribute

Information Fusion Technical Area Overview & Applications

Joseph A Karakowski

[email protected]

(732)460-7752

November 16, 2011


What we will cover..

the “important” parts…..about fusion


Agenda

• Background Technology– Fusion Definition– Fusion Models

• Fusion Technology Sector Applications– Military– Medical & Non-Military

• Personal Fusion Areas (Optional)

Questions to be Answered:

What is Fusion Technology and it’s basis?

What are some example fusion applications in specific markets?


Some Fusion Definitions……the process of combining data/information to

estimate or predict the state of some aspect of the world (Bowman)

…the process of utilising one or more data sources over time to assemble a representation of aspects of interest in an environment (Lambert)

…series of processes performed to transform observational data into more detailed and refined information, knowledge, and understanding (USArmy)

…everything is Connected… a “Global Graph” portrays the connected world; graph nodes are the entities; graph links are the actions or relationships (Walsh)


What is Fusion?

Technical methods/processes which supports, through cognitive/perceptual modeling, the solution of a class

“Difficult Problems”

Some Typical characteristic of Difficult Problems:• Multiple Goals• Complexity, with large numbers of items, interrelations and decisions• Dynamic, time considerations• Cognitive/perceptual problem solving

These are a first scientific step to solve these classes of problems, which have not been solvable, up to this time.

Implementation of these processes using information technology, has been moving forward for the last 25+

years, and will probably continue for many more years…


“The Blind Man & the Elephant”Question: What is an Elephant?


It’s the JDL

Model !

It’s a Cognitive/Perceptual Process!

It’s Intelligence

Apps !

The “Fusion Elephant”

It’s a Global Graph !

Its Biometric

Apps!

A State Prediction Problem!

Question: What is Fusion?


A More Realistic “Fusion Elephant”

Nuclear

This is a new technology, and

much RD&E remains


Major Fusion Process Models

• Joint Directors of Laboratories Model (JDL)* [1986-Pres]

• Transformation of Requirements for Information Process (TRIP) Model [2000-?]

• Visual Situation Assessment Model () [1997]

• Salerno SA Model [2001-Pres]

• “Graph” Fusion Model [2005-Pres]

• Contextual Fusion Model * [2009-Pres]

• There are many others….

Many Definitions…and many more models have been proposed and built!


ProcessRefinement

Level 4

SourceInput

Human/ComputerInteraction

Preprocessing/Predetection

FusionLevel 0

Single ObjectRefinement

Level 1

Location;Attributes

Behavior;Class; ID

Aggregateobject

refinement

Situationinterpretation

Intent;Vulnerability

Courses ofAction

SituationRefinement

Level 2

Implications/Threat

RefinementLevel 3

Database Services

Relatively statica priori

Knowledge

DynamicSituationDatabase

DFG Functional Model (JDL Model)

Richard Antony, in DFG Meeting Minutes, W. Doig, 14 March 1997


Conceptually organized along three(related) dimensions: (entity, context1, context2)

AKA “Triple”

“Fusion” ..an “assessment” operation between pairs of Triples: Lead to 8 fundamental classes of fusion operations

Antony & Karakowski Contextual Fusion Model (CFM) 2009

CFM explicitly fuses diverse context with specific basic entities, all within a computational JDL model framework,

resulting in a testable, expandable, and general fusion model

“Fusion as a Process” exhausts all possible “assessment” combinations or fusion in a Triple; the result is a set of discovered concepts & relations from the fusion of the pairs of Triples. This provides a rich discovery space within an existing knowledge source


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• Information for fusion requires both context and entity• “Entity” is the specific unit of information, or node in a

graphical representation • Context allows perception of an Entity with respect to the

information of interest– Context gives meaning to an Entity’s “information”– Context is required before an information entity has any

meaning– Context must be an integral part of the fusion process

& process model, its computation paradigm

Context is knowledge that enhances the more complete understanding of a specific entity of interest and the desired resultant objective information product (s)

Contextual Fusion Model Context & Content


Two Entities within a graph, with Two sets of Two Entities, as their Contexts

Green = Entity

E2

E3/C3 E4/C4

E1

E5/C5 E6/C6

Entity Graph Nodes

Graph Entity-Entiy Relations

Entities as Context


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FormIndividual

Entity Location TimeExample Concept of Fusion Result formed for Location and time context

1 Similar Similar SimilarFuse multiple, similar information sources;

tracking using E-sensors

2 Similar Similar Dissimilar Static similar object

3 Similar Dissimilar Similar Infeasible condition – inconsistency; a truth

maintenance signal

4 Similar Dissimilar DissimilarEntity tracking/ tracking using both (slower) E-

sensors and messages

5 Dissimilar Similar SimilarAssociation / correlation of possible action as co-

located Entities

6 Dissimilar Similar Dissimilar Association / correlation of entities based on

same location only

7 Dissimilar Dissimilar Similar With prior Communication info: Potential Entity

comm link (cell phone, chat, email)

8 Dissimilar Dissimilar Dissimilar

Multiple different entities at different times- further data mining and other FF conceptual analyses may be indicated

Fusion Operation = (entity A, location A , time A) x (entity B, location B, time B)

Fusion Operation = fusion of two entities with associated context

Level 1 Fusion

Level 2 Fusion

Eight Canonical Fusion FormsIntelligence Traditional Tracking/Correlation Application

Contextual Dimensions Similarity/Dissim Assessment Op


Simple Physical Context Example - Voltage

Voltage

time.

Human Entity, for example, a much more complex entity…. This is like a generalization from “humans” to “human signals”

If a voltage(entity) is viewed by itself without any context, we just “see” a

value, either static, “semantic” or apparently varying

If voltage has added the context of time, “signals” are created, with the field of electrical electronic engineering and associated signal analysis….Note the huge information content difference between the entity of “voltage” and the addition of the context “time” and how context gives much more “meaning” to the entity (voltage)


16

FormV-SourceCompare Location Time

Concept of Fusion Result of Voltage Sourcefor Location and time context

1 Similar Similar Similar Instantaneous single source value, at one place

2 Similar Similar Dissimilar Time varying single source value, at one place

3 Similar Dissimilar Similar Instantaneous V-field for single source

4 Similar Dissimilar DissimilarInstantaneous time-varying V-field for single

source

5 Dissimilar Similar SimilarInstantaneous multiple source value, at one place

6 Dissimilar Similar Dissimilar Time varying / multiple sources value, at one

place

7 Dissimilar Dissimilar Similar Instantaneous V-field for multiple sources

8 Dissimilar Dissimilar DissimilarInstantaneous time-varying V-field for multiple

sources

Fusion Operation = ( Source V1, location V1 , time V1) x (Source V2, location V2, time V2)

Level 1 Fusion

Level 2 Fusion

Eight Canonical Fusion FormsSource “Voltage” (just for fun)

Contextual Dimensions


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Individuals

Organizations

All Relations based on

Location & Time Context Only

Events

Prior Art: Military Target Entity Model

DRs of Individuals to EventsLevel 2

DRs of Individuals to Organizations

Level 2

DRs of Organizations

to EventsLevel 2

DRs of Events to

Events

DRs of Organization

to Organization

DRs of Individuals

to Individuals

DR: Discovered Relations thru contextual FFs

Other forms of discovery are possible;

(I O, OE ) (EI ) eg


Fusion Applications


Some Fusion Military Application Areas

• Intelligence

• Bio sensing/biometrics

• Situation Awareness

• Imagery

• SIGINT(COMINT/ELINT)

• Tracking

Can support at all levels: Hardware,

Software, Level 0 – Level 5


Non-Military Fusion Application Areas

• Networking/Cellular

• Homeland Security

• Medicine

• Chemistry

• Cognitive sciences

• …many others…


Fusion “Topics” from a recent conference…


Conference [Shortened-”C”] Index of Fusion Topics I

• Camera• Capability Acquisition Graph• CBRN data fusion• Cellular automata• Centralized processing

systems• Challenge Problem Set• Change detection• Chemical plume• Classification fusion• Classification System• Closest point approach

• Clustering algorithm• Clutter• Co-ranking• Coalition formation• Coalition operations• Coarsening• Coastal radar• Cognitive Radio Networks• Collaborative systems• Collision mitigation• Color Clustering• ……


• Combination of belief functions

• Combinatory categorial grammar

• Communication Decision• Communication failures• Complex object recognition• Compression• Computer security• Conceptual graphs

• Conditional independence• Confidence management• Configuration• Conflict analysis• Confusion• Conjunctive operator• Connection Model• Context• Contradiction• Convex optimization• Convoy tracking• Cooperative systems• Coordinate registration

Conference [Shortened-”C”] Index of Fusion Topics II


• Coordination• Correlation• Course Of Action• Covariance control• Credal networks• Credibility• Crop modeling• Cross correlation• Cross-cueing• Cubic Spline Curve• Cued Sensors• Cyber fusion• Cyber-security

Conference [Shortened-”C”] Index of Fusion Topics III

From these three slides one can see both very specialized areas and much broader areas

which currently utilize information Fusion

technology


Overview of Specific IF Apps from Selected market areas

Military1…Biometrics2…Target Detection & Tracking3…Chemical & Explosives4…Image Fusion

Medical5…Breast Cancer6…Radiology

Non-Military 7…Dept of Homeland Security8…Cyber Security

Summaries of specific fusion papers follows…

Note: All these apps will fall somewhere in the fusion models and

fusion definitions which I previously

described.


1. BiometricsA Multibiometric Face Recognition Fusion

Framework with Template Protection[1]• “A fusion framework.. which demonstrates how …algorithms that

produce hard decisions can be combined with unprotected algorithms that produce scores or soft decisions”

Military & Commercial

Improving the recognition of fingerprint biometric system using enhanced image fusion[2]•“approach to increase the verification and identification of fingerprint recognition. This was achieved by using … linear fusion techniques”

Multimodal Eye Recognition[3]• “results show that the proposed eye recognition method can achieve better performance…, and the accuracy of…kernel-based matching score fusion methods is higher than PCA and LDA”


2. Target Detection &Tracking

Level 0-2 fusion model for ATR using fuzzy logic[4]

• “use of fusion at the lowest levels has been demonstrated. …provides a structure for fusion of multispectral data at all levels”

Military

Long-duration Fused Feature Learning Aided Tracking[5]• “Our experiments indicate that the Long-term Hypothesis Tree algorithm, which solves the tracklet-to-tracklet association problem, can be used to strongly disambiguate a multitude of situations and is a more computationally efficient algorithm than previously proposed joint solutions”


3. Chemical & Explosives

Fusing chlorophyll fluorescence and plant canopy reflectance to detect TNT contamination in soils[6]

• “physiological response of plants grown in TNT contaminated soils and … to detect uptake in plant leaves…use remote sensing of plant canopies to detect TNT soil contamination prior to visible signs”

Military Market

Sensor data fusion for spectroscopy-based detection of explosives[7]• “Multi-spot fusion is performed on a set of independent samples from the same region…. Furthermore, the results … are fused using linear discriminators. Improved detection performance with significantly reduced false alarm rates is reported using fusion techniques”


4. Image Fusion

Towards Visual-Data Fusion[8]• “Fusion for both data and visual processes are derived as

specific transforms from human linguistic requests. Visual “understanding” occurs by human-directed perception of summarized pattern representations within a familiar frame of reference”

Military & Commercial

An orientation-based fusion algorithm for multisensor image fusion[9]• “Gabor wavelet transform … to fuse visible images and thermal images; orientation-based fusion is superior to the results of multiscale fusion algorithms…and can be applied to multiple (more than two) image fusion”


5. Breast Cancer

Investigation of PET/MRI Image Fusion Schemes for Enhanced Breast Cancer Diagnosis[10]

• “results indicate that the radiologists were better able to perform a series of tasks when reading the fused PET/MRI data sets using color tables generated by our new genetic algorithm, as compared to commonly used …schemes”

Medical

Time of Arrival Data Fusion Method for Two- Dimensional Ultrawideband Breast Cancer Detection[11]•“A new microwave imaging method is given for breast tumor detection using an ultrawideband (UWB) imaging system. By combining the time of arrival (TOA) measurements from different sensors, the presence and location of small malignant lesions can be identified”


6. Radiology

KNOWLEDGE BASED FUZZY INFORMATION FUSION APPLIED TO CLASSIFICATION OF ABNORMAL BRAIN TISSUES FROM MRI[12]

• “automatically classify abnormal tissues in human brain in a three dimension space from multispectral magnetic resonance images such as TI-weighted. T2- weighted and proton density feature images. It consists of four steps: data matching. information modeling, information fusion and fuzzy classification”

Medical – Add

New Applications of Planar Image Fusion in Clinical Nuclear Medicine and Radiology[13]• Fusion of multiple modalities has become an integral part of modern imaging methodology, especially in nuclear medicine where PET and SPECT scanning are frequently paired with computed tomography(CT). Additional fusing of orthopedic radiographs with photographic images of the extremities..


7. DHSInformation Fusion for CB Defense Applications[14]• “With appropriate algorithmic approaches and appropriately resolved

tradeoffs, information fusion can offer… the potential of reaching performance that would be difficult, if not impossible, to attain otherwise. Thus, information fusion represents a significant

opportunity for the CB defense and homeland security realm”

Military & Non-Military

Decision-level Information Fusion to Assess Threat Likelihood in Shipped Containers[15]•“details an approach to the decision-level fusion of disparate information to produce an assessment of the presence of a threat in a shipping container”

Homeland Security Fusion Application of STEF[16] •“fusion system provided sufficient actionable intelligence that could have stopped a .. realistically staged terrorist attack on a US civilian target. …provided sufficient information to allow .. arresting the mastermind of the plot, as well as other key individuals and detaining the lower level individuals in his network, including the suicide bomber”


8. Cyber Security

Application of the JDL Data Fusion Process Model for Cyber Security[17]

• “explores the underlying processes identified in the Joint Directors of Laboratories (JDL) data fusion process model and further describes them in a cyber security context”

Military & Non-Military


We have covered “the more important parts”…a warning


Summary & Closing Comments

• Short background of Fusion Technology & Models/Contextual Fusion Model

• Few examples of Fusion R&D / Apps

• A lot was left out ….


Backups


Some of My Personal Fusion RDE Areas

• UGS tracking/ID L0/L1• RADAR ID

– Signal processing L0/L1 / Fuzzy Expert– Confirmation/Disconfirmation

• Voice Fingerprint ID biometrics L0/L1• Visual fusion L0-L2[*]• Semantic/contextual unstructured information --

understanding & discovery L1-L3[*]• Contextual Fusion System[2006-2010]• General Context Fusion Model [2011-?]


Some Fusion Publications

• Karakowski, J.A., “An Application of Text-Independent Speaker Recognition to High Speed Voice Surveillance”, Wide Area Surveillance Symposium, Office of Nat’l Drug Control Policy/Counter Drug Technology Assessment Center(1993)

• Karakowski, J.A., “Text Independent Speaker Recognition using A Fuzzy Hypercube Classifier”, ICASSP97(1997)

• Karakowski, J.A., “Towards Visual Fusion”, Invited Paper, Georgia Tech(1998).• Antony, R. T. and Karakowski, J. A., “Service-Based Extensions to the JDL Fusion

Model,” SPIE Defense Security and Sensing Conference (March 2008).• Antony, R. T. and Karakowski, J. A., “Fusion of HUMINT & Conventional Multi-Source

Data,” National Symposium on Sensor and Data Fusion, Session SC04 pp. 1-16 (07).• Antony, R. T. & Karakowski, J. A., (2007) “Towards Greater Consciousness in Data

Fusion Systems,” MSS National Symposium on Sensor and Data Fusion, (June 07).• Antony, R. T. and Karakowski, J. A., “First-Principle Approach to Functionally

Decomposing the JDL Fusion Model: Emphasis on Soft Target Data,” Fusion (July 08).• Antony, R. T. & Karakowski, J. A., “Discovery Tools for Soft Target Applications,”

National Symposium on Sensor and Data Fusion(2009)• Antony, R. T. and Karakowski, J. A., “First-Principles Mapping of Fusion Applications

into the JDL Model,” SPIE Defense Security and Sensing Conference (April 2009)• Antony, R.T, & Karakowski, J.A., “Multiple Level-of-Abstraction Tracking and Alias

Resolution”, National Symposium on Sensor and Data Fusion(2010)• Antony, R.T., & Karakowski, J.A., “Toward more Robust Exploitation of the Asymmetric

Threat: Binary Fusion Class Extensions”, (April 2011) SPIE.

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