Summer Training Baljeet
-
Upload
kartikay-kulshrestha -
Category
Documents
-
view
234 -
download
0
Transcript of Summer Training Baljeet
-
8/4/2019 Summer Training Baljeet
1/28
SUMMER TRAINING REPORT
(13th June to 12th July 2011)
ON
IMPLEMENTATION OF PLATEAU
HISTOGRAM EQUALIZATION ALGOROTHIM
FOR IMAGE ENHANCEMENT USING
MATLAB
Training undertaken at:Training undertaken at:
Instruments Research and Development EstablishmentInstruments Research and Development Establishment
DRDO, MINISTRY OF DEFENCEDRDO, MINISTRY OF DEFENCE
GOVERNMENT OF INDIAGOVERNMENT OF INDIA
Raipur RoadRaipur Road, Dehradun, Dehradun
Submitted by:
Ravi Singh
B.Tech 3rd yr, Electronic & Communication Engg.
Graphic Era University
(Dehradun)
-
8/4/2019 Summer Training Baljeet
2/28
ACKNOWLEDGEMENT
First of all I would like to express my sincere gratitude to Shri S.S.
SUNDARAM, DIRECTOR IRDE, Dehradun, for giving me the
opportunity of working in this esteemed organization.
I would also like to thank Dr. S.S. NEGI, ASSOCIATE
DIRECTOR TIS division, for giving me the opportunity to work in
THERMAL IMAGING SYSTEM DIVISION (TIS) and also, for helping
me in selecting the right environment for working and allowing me the
project IMPLEMENTATION OF CENTROID ALGORITHM FOR
TARGET TRACKING.
This acknowledgement would be incomplete without mentioning the
name of Mr. Dinesh Chander Singh, Sc. C, who not only guided methroughout the research work, but also provided the inspiration to try and
accomplish the task assigned.
I would also like to present my sincerest thanks to all the members,
staff and officers of the TIS DIVISION for providing a helping hand at the
time of need.
Date: (ravi Singh)
-
8/4/2019 Summer Training Baljeet
3/28
CERTIFICATE
This is to certify that Mr.ravi Singh, student ofELECTRONIC &
COMMUNICATION ENGINEERING, B.TECH IIIrd YEAR,
GRAPHIC ERA UNIVERSITY, has undergone training at TIS Division of
I.R.D.E., Dehradun from 13th June to 12th July 2011 .During this period, he
was assigned the work IMPLEMENTATION OF PLATEAU
HISTOGRAM EQUALIZATION ALGOROTHIM FOR
IMAGE ENHANCEMENT USING MATLAB. He has
completed his work and his work has been satisfactory during training.
We wish him a prosperous career and success in life.
(Dinesh Chander Singh) (Dr S.S. Negi)
Scientist C Associate Director
TIS Division TIS DivisionIRDE Dehradun IRDE Dehradun
-
8/4/2019 Summer Training Baljeet
4/28
CONTENTS
INTRODUCTION TO IRDE
PRINCIPLE OF THERMAL IMAGING SYSTEM
ELEMENTS OF THERMAL IMAGING SYSTEM
MATLAB OVERVIEW
DIGITAL IMAGE PROCESSING
IMAGE HISTOGRAM
PLATEAU HISTOGRAM EQILIZATIONALGORITHM
MATLAB CODE
RESULT AND ANALYSIS
CONCLUSION
REFERENCES
-
8/4/2019 Summer Training Baljeet
5/28
INTRODUCTION TO IRDE
Instruments Research and Development Establishment (IRDE), a major
equipment oriented establishment within Defence Research and Developmentorganization, came into existence in its present form as an institute devoted
exclusively to research and development in the field of instrumentation for the
services on 1st December 1961. It, however, has an earlier history as a
composite establishment performing dual role of R&D and inspection.
1. Its origin can be traced back to the year 1939, when inspectorate of
scientific stores was formed at Rawalpindi for the inspection of
telecommunication equipments, used by Indian Army.
2. It underwent changes taking the shape of Technical Development
Establishment (Instruments and Electronics). With the increase in tempo
of R&D work the responsibility to meet over increasing and exacting
requirements of services in the respect of more and more advanced and
sophisticated equipment, the establishment was upgraded to I.R.D.E. in
February 1960.
CHARTER OF WORK
R&D, Design and Technology in the fields of Optical, Electro-
Optical & Optronic Instrumentation, Fire Control Systems, Infrared search
and Track and Stand Alone Surveillance Systems.
R&D in applied optics including Optics Design, Optics
Technology, Thin Films, Night Vision, Fiber Optics, Integrated & Non-
Linear Optics, Holography and Optical signal processing etc.
-
8/4/2019 Summer Training Baljeet
6/28
MAJOR AREAS OF ACTIVITIES
Technology and product development:
1. Thermal Imaging
2. Night Vision Instrumentation3. Laser Ranging & Instrumentation
4. Servo-stabilization Systems
5. Fire Control Systems
6. Software and Microprocessors
7. Photonics and its Applications
SPECIALIZED TECHNOLOGY SUPPORT
1. Optics Design and Fabrication
2. Thin Film Technology
3. Precision Mechanism
A large number of instruments for use by services have been designed &
developed by the establishment and most of them are now in regular production
with Ordnance Factories and other production agencies.
Specialized groups of laboratories have been created in the establishment,
which is devoted to the design and development of various types of instruments
for application like sighting, vision, ranging and engagement etc.
Though essentially equipment oriented laboratory I.R.D.E. has to its
credit significant contribution in the fields of basic research in different areas of
optical & theoretical research, studies conducted in holography, fiber optics,
optical-imagery and spectroscopic study of materials in optical and infrared
regions.
-
8/4/2019 Summer Training Baljeet
7/28
Apart from normal R&D work connected with design and development
of equipment for services use and development of allied and associated
technologies, the establishment also has the role of undertaking investigation on
major modification either with a view to extending the role of instruments of
enhancing their useful life, prepare manufacturing particulars and assist the
services in evolution of foreign equipment pertaining to its field of activity.
The establishment is also responsible for the transfer of technology to
firms both in public and private sectors for creating production base in country
for sophisticated instruments developed by it. For this purpose a Technology
Transfer Center (TTC) has been set up at the establishment for smooth transfer
of new technologies evolved in the field of electro-optical instrumentation to
concerned production engineer.
ORGANIZATIONAL SETUP
Theestablishment has been organized into 10 technical divisions and 8
independent groups, directly responsible to the director apart from
administrative, financial and security aspects.
INFRASTRUTURE
The establishment has well equipped laboratories looking after the development
of optical instruments, night vision equipments, holographic systems, thermal
imaging systems etc. All these laboratories are equipped with sophisticated and
modern test equipments and devices.
LIBRARY & DOCUMENTATION SERVICE
The establishment has well-equipped reprographic section to look after printing
and reproduction work of technical documents and drawing. It also has a well-
stocked technical laboratory.
-
8/4/2019 Summer Training Baljeet
8/28
PRINCIPLE OF THERMAL IMGING SYSTEM
Thermal imaging system as mentioned earlier picks up the IR radiation emitted
by the objects, which are at temperature above zero Kelvin and also due to
background temperature and emissivity differences. IR radiations are a part of
the electromagnetic spectrum pertaining to the IR region and that the spectral
radiant emittance curves in accordance with the Plancks black body law, it may
be observed that targets at ambient temperature of 300K emit maximum amount
of radiation at 10m, whereas the hotter targets like jet exhausts at 700K have
peak spectral emission at around 4m.
At wavelengths other than those of atmospheric windows i.e. between 2
to 20 m the water vapor and CO2 molecules present in the atmosphere
strongly absorb the IR radiation. A thermal sight employing IR detectors
sensitive in the spectral bands convert the IR radiation emitted by the target into
electrical signals. The detector output after suitable processing is again
converted into a visible image either using a CRT displays or LED display.
-
8/4/2019 Summer Training Baljeet
9/28
ELEMENTS OF THERMAL IMAGING SYSTEM
1. Sources of Thermal Radiations
Thermal imaging uses long mid and long wavelength IR because these
occur naturally and are readily transmitted through the atmosphere. The objects
in the scene are themselves the sources of radiations making up the image. It
relies on the observed phenomenon that an objects glows when hot.
Also when the temperatures of these objects fall, the range of wavelength of
the objects shifts towards the red and of the spectrum. With optics and detectors
that work into the IR we would continue to see the element low even as it is
cooled to the room temperature and it is in this property that true benefits of the
Thermal Imaging are realized. Objects at normal temperature glow even viewed
in the infrared and so without the need for external illumination, glow shows
even in dark.
2. Optics
Optical system is required to produce a focused image of a distant scene
while eliminating radiations from sources outside the scene. Baffles are used to
eliminate stray radiation inside the optical system and because the system is a
thermal imager it is necessary to fully absorb the stray radiations. Lenses may
be used to collect and focus the radiation from the sources. Typically a multi
element design is required so that optical aberration can be minimized.
-
8/4/2019 Summer Training Baljeet
10/28
3. Detectors
They have a simple function i.e. to absorb the thermal radiations from the
part of scene and convert it to the electrical signal. The strength of the signal is
then the measure of the temperature of that part of the scene. By compiling the
outputs from many detectors, or moving the scene across the detectors, a
complete image of the scene is obtained.
4. Cooling
It is necessary to dissipate the heat of the detector array, which may get
heated due to the incident infrared radiations. The cooling may be obtained byeither of the following methods
Pettier cooling
Joule cooling
Stirling cooling
Most commonly the stirling cooling is adopted in the Thermal Imaging
systems.
5. Signal processor and display
In signal processor electrical signals, form IR detector is signal
conditioned, digitized and process to form real time image. The display is
generally the CRT display. The amplified signal form the detector array is
multiplexed to get single line video, which can, then be fed, into the CRT to get
IR image of the scene in the CRT. Video can also be made compatible to TV
using a digital scan converter and displayed onto a standard monitor.
-
8/4/2019 Summer Training Baljeet
11/28
MATLAB OVERVIEW
MATLAB is a high-performance tool for technical computing. It
integrates computation, visualization, and programming in an easy-to-use
environment where problems and solutions are expressed in familiar
mathematical notation. Typical uses include:
1. Math and computation
2. Algorithm development
3. Data acquisition
4. Modeling, simulation, and prototyping
5. Data analysis, exploration, and visualization
6. Scientific and engineering graphics
7. Application development, including graphical user interface building
MATLAB is an interactive tool whose basic data element is an array that does
not require dimensioning. This allows you to solve many technical computing
problems, especially those with matrix and vector formulations, in a fraction of
the time it would take to write a program in a scalar non-interactive language
such as C orFORTRAN.
The name MATLAB stands for Matrix Laboratory. MATLAB was originally
written to provide easy access to matrix software developed by the LINPACK
and EISPACK projects, which together represent the state-of- the art in
software for matrix computation.
-
8/4/2019 Summer Training Baljeet
12/28
MATLAB has evolved over a period of years with input from many
users. In university environments, it is the standard instructional tool for
introductory and advanced courses in mathematics, engineering, and science. In
industry, MATLAB is the tool of choice for high-productivity research,
development, and analysis.
MATLAB features a family of application-specific solutions called
toolboxes. Very important to most users ofMATLAB, toolboxes allow you to
learn and apply specialized technology. Toolboxes are comprehensive
collections of MATLAB functions (M-files) that extend the MATLAB
environment to solve particular classes of problems. Areas in which toolboxes
are available include signal processing, control systems, neural networks, fuzzy
logic, wavelets, simulation, and many others.
The MATLAB System
The MATLAB system consists of five main parts:-
1. Desktop Tools and Development Environment:
This is the set of tools and facilities that help you use MATLAB functions and
files. Many of these tools are graphical user interfaces. It includes the
MATLAB desktop and Command Window, a command history, an editor and
debugger, and browsers for viewing help, the workspace, files, and the search
path.
2. The MATLAB Mathematical Function Library:
This is a vast collection of computational algorithms ranging from elementary
functions like sum, sine, cosine, and complex arithmetic, to more sophisticated
functions like matrix inverse, matrix eigenvalues, Bessel functions, and fast
Fourier transforms.
3. The MATLAB Language:
-
8/4/2019 Summer Training Baljeet
13/28
This is a high-level matrix/array language with control flow statements,
functions, data structures, input/output, and object-oriented programming
features. It allows both "programming in the small" to rapidly create quick and
dirty throw-away programs, and "programming in the large" to create complete
large and complex application programs.
4. Graphics:
MATLAB has extensive facilities for displaying vectors and matrices as graphs,
as well as annotating and printing these graphs. It includes high-level functions
for two-dimensional and three-dimensional data visualization, image
processing, animation, and presentation graphics. It also includes low-level
functions that allow you to fully customize the appearance of graphics as well
as to build complete graphical user interfaces on your MATLAB applications.
5. The MATLAB Application Program Interface (API):
This is a library that allows you to write C and FORTRAN programs that
interact with MATLAB. It includes facilities for calling routines from
MATLAB (dynamic linking), calling MATLAB as a computational engine, and
for reading and writing MAT-files. In the case of tracking, here we use image-
processing toolbox of MATLAB where there are many functions that will help
us during programming.
-
8/4/2019 Summer Training Baljeet
14/28
DIGITAL IMAGE PROCESSING
A thermal image is a continuously varying array of gray shades; shades
vary from light to dark. All images are made of gray shades ranging from black
to white.
A digital image is composed of discreet points of gray tone or brightness, rather
than continuously varying levels. Gray value of each pixel along a line is
summed together & accumulated sum is subtracted from the previously
accumulated sum of the same image.
In a digital image, each pixel has a brightness value. To make a digital image
from a continuous image, it must be divided into individual points of
brightness. Each point of brightness must be described by a digital data value.The process of breaking up a continuous image & determining digital
brightness values are referred to assampling& quantization respectively.
-
8/4/2019 Summer Training Baljeet
15/28
The sampling process samples the intensity of a continuous image at
specific locations. The quantization process determines the digital brightness
values of each sample, ranging from black, through gray scales to white.
A quantized sample is referred to as a picture element, or pixel because it
represents a discreet digital element of digital image. The combination of
sampling & quantization process is referred to as image digitization.
An image is generally sampled into a rectangular array of pixels. Each pixel has
an (x,y) coordinates that corresponds to its location with in the image. The x-
coordinate is pixels horizontal location; the y- coordinate is its vertical
location. The pixel with coordinate (0,0) is the upper left corner of the image.
We have used 8- bit image, i.e. it contains 256 gray levels (0 to 25An in-
focused image consists of sharp images, which contains rapid brightness
transitions. Slowly varying brightness transitions represent out-of-focused
image.
Pictures are the most common and convenient means of conveying and
transmitting information. Pictures concisely convey information about position,
sizes and inter relationships between objects. Human beings are good at
deriving information from such images because of our innate visual and mental
abilities. About 75% of information received by human is in pictorial form. In
the present context, the analysis of pictures that employ an overhead
perspective, including the radiation not visible to human eye are considered.
Thus, the focus will be on analysis of remotely sensed images. These images
are represented in digital form. When represented as numbers brightness can be
added, subtracted, multiplied, divided and, in general, subjected to statistical
-
8/4/2019 Summer Training Baljeet
16/28
manipulations that are not possible if an image is presented only as a
photograph.
An image may be defined as two dimensional function, f(x, y ), where x & y
are spatial co-ordinates, and the amplitude of f at any pair of co-ordinate is
called the intensity or gray level of the image at that point. When x, y, and the
amplitude values of f are finite, discrete quantities, we call the image a digital
image.
A digital image is composed of finite number of elements, each of which has a
particular location and value. These elements are referred to as picture
elements, image elements and pixels. Pixel is a term most widely used to denote
the elements of digital image. Digital image processing refers to processing
digital images by means of a digital computer.
There are three types of computerized processes: low, mid and high level.
Low-level processes involve primitive operations such as image pre-
processing to reduce noise, contrast enhancement and image sharpening.
Its inputs as well as outputs are both images.
Mid-level processes on images involve tasks such as image segmentation.
It is characterized by the fact that its inputs generally are images but its
outputs are attributes extracted from those images. Higher level processing involves making sense of an ensemble of
recognized objects.
-
8/4/2019 Summer Training Baljeet
17/28
FUNDAMENTAL CLASSES OF DIGITAL IMAGE PROCESSING
Digital image processing operations can be grouped into five fundamental
classes:
1. Image enhancement
2. Image restoration
3. Image analysis
4. Image compression
5. Image synthesis
IMAGE HISTOGRAM
An image histogram is a type ofhistogram that acts as a graphical
representation of the tonal distribution in a digital image. It plots the number
ofpixels for each tonal value. By looking at the histogram for a specific image
a viewer will be able to judge the entire tonal distribution at a glance.
Image histograms are present on many modern digital cameras. Photographers
can use them as an aid to show the distribution of tones captured, and whether
image detail has been lost to blown-out highlights or blacked-out shadows.[2]
The horizontal axis of the graph represents the tonal variations, while
the vertical axis represents the number of pixels in that particular tone.[1] The
left side of the horizontal axis represents the black and dark areas, the middle
http://en.wikipedia.org/wiki/Histogramhttp://en.wikipedia.org/wiki/Graphical_representationhttp://en.wikipedia.org/wiki/Graphical_representationhttp://en.wikipedia.org/wiki/Lightness_(color)http://en.wikipedia.org/wiki/Digital_imagehttp://en.wikipedia.org/wiki/Pixelshttp://en.wikipedia.org/wiki/Digital_camerashttp://en.wikipedia.org/wiki/Image_histogram#cite_note-1http://en.wikipedia.org/wiki/Horizontal_axishttp://en.wikipedia.org/wiki/Graphicshttp://en.wikipedia.org/wiki/Vertical_axishttp://en.wikipedia.org/wiki/Image_histogram#cite_note-sutton-0http://en.wikipedia.org/wiki/Histogramhttp://en.wikipedia.org/wiki/Graphical_representationhttp://en.wikipedia.org/wiki/Graphical_representationhttp://en.wikipedia.org/wiki/Lightness_(color)http://en.wikipedia.org/wiki/Digital_imagehttp://en.wikipedia.org/wiki/Pixelshttp://en.wikipedia.org/wiki/Digital_camerashttp://en.wikipedia.org/wiki/Image_histogram#cite_note-1http://en.wikipedia.org/wiki/Horizontal_axishttp://en.wikipedia.org/wiki/Graphicshttp://en.wikipedia.org/wiki/Vertical_axishttp://en.wikipedia.org/wiki/Image_histogram#cite_note-sutton-0 -
8/4/2019 Summer Training Baljeet
18/28
represents medium grey and the right hand side represents light and pure white
areas. The vertical axis represents the size of the area that is captured in each
one of these zones. Thus, the histogram for a very bright image with few dark
areas and/or shadows will have most of its data points on the right side and
center of the graph. Conversely, the histogram for a very dark image will have
the majority of its data points on the left side and center of the graph.
Image enhancement using plateau histogram equalization
algorithm
This self-adaptive contrast enhancement algorithm is based on plateau
histogram equalization for infrared images. By analyzing the histogram of
image, the threshold value is got self-adaptively. This new algorithm can
enhance the contrast of targets in most infrared images greatly. The new
algorithm has very small computational complexity while still produces highcontrast output images, which makes it ideal to be implemented byFPGA (Field
Programmable Gate Array) for real-time image process. This paper describes a
simple and effective implementation of the proposed algorithm, including its
threshold value calculation, by using pipeline and parallel computation
architecture. The proposed algorithm is used to enhance the contrast of infrared
-
8/4/2019 Summer Training Baljeet
19/28
images generated from an infrared focal plane array system and image contrast
is improved significantly. Theoretical analysis and other experimental results
also show that it is a very effective enhancement algorithm for most infrared
images.
An infrared image is created from infrared radiation of objects and their
backgrounds. Generally the temperature difference between target objects and
their background is small, and the temperature of background is high, which
result in the fact that most infrared images have highly bright back-ground and
low contrast between background and targets. In order to recognize targets
correctly from these images, good enhancement algorithms must be applied
firstly. Gray stretch, histogram equalization are general image enhancement
algorithms. And histogram equalization is a widely used enhancement
algorithm, in which the contrast of an image is enhanced by adjusting gray
levels according to its cumulative histogram. But histogram equalization
algorithm is not applicable to many infrared images, because the algorithm
often mainly enhances image background instead of targets. In an effort to
overcome this problem, Virgil E. Vichers and Silverman, proposed two new
histogram-based algorithms:
plateau histogram equalizationand
histogram projection
Plateau histogram equalization has been proven to be more effective, which
suppresses the enhancement of background by using a plateau threshold value.
But the plateau threshold value is an empirical value in general which limits the
algorithms practical usage. A modification is made to plateau histogram
-
8/4/2019 Summer Training Baljeet
20/28
equalization. By analyzing the histogram of infrared images, an estimated value
of plateau threshold value is got self-adaptively. This modified algorithm is able
to enhance the contrast of target objects in most infrared images more
effectively than the original algorithm. It has very small computational
complexity while still produces high contrast output images, which makes it
ideal to be implemented by FPGA for real-time imaging applications. This
describes an implementation of our proposed algorithm, including its plateau
threshold value calculation by using pipeline and parallel computation
architecture. The proposed algorithm has been used to enhance infrared images
generated from an infrared focal plane array system and the contrast of image
has been improved significantly.
The principles of self-adaptive plateau Histogram
equalization
Plateau histogram equalization
Plateau histogram equalization is a modification of histogram equalization,
proposed by Virgil. Vichers and Silverman. An appropriate thresh-old value is
selected firstly, which is represented as
-
8/4/2019 Summer Training Baljeet
21/28
Selection of self-adaptive plateau threshold Value
Selection of plateau threshold value is very important in the infrared image
enhancement algorithm of plateau histogram equalization. It would have effect
on the contrast enhancement of images. Appropriate plateau threshold value
would greatly enhance the contrast of image. In addition, some plateau value
would be appropriate to some infrared images, but not appropriate to others. As
a result, the plateau threshold value would be selected self-adaptively according
to different infrared images in the process of image enhancement.
MATLAB CODE
PROGRAM FOR ALGORITHM
Clear all
-
8/4/2019 Summer Training Baljeet
22/28
P=100; % set the pixel parameter from 1 to (n1*n2)
A=imread (c:\image\256_256bmp);
B=rgb2gray(a);
Figure (1);
imshow(b);
b=double(b);
[n1,n2] = size(b); % read the size of b
For j= 1:256
k(i)=0;
c(i)=0;
d(i)=0;
ds(i)=0;
end
for i = 1:n1
each
For j=1:n2
d=b(i,j);
k(:,d+1)=k(:,d+1)+1; % count the no of pixel level(intensity)
end
end
figure(2);
plot(k); %plot the histogram
xlabel(GRAYSCALE VALUES);
-
8/4/2019 Summer Training Baljeet
23/28
ylabel(NUMBER OF PIXEL);
for i =1:256
c(i)=main(k(i),P);
end
figure(4);
plot(c);
for i =1:256
sum=0;
for j=1:i
sum = sum+c(j);
end
d(i)=sum;
end
for i=1:256
ds(i)=floor((256*d(i)/d(256));
end
for i=1:n1
for J=1:n2
kk=b(i,j);
c(i,j)=ds(:,k+1)
end
end
figure(3);
imshow(c,[]);
-
8/4/2019 Summer Training Baljeet
24/28
Results and analysis
In this Algorithm, images are enhanced by histogram equalization, self-
adaptive plateau histogram equalization respectively. Then a comparison is
made between the original image and the enhanced image.
The original histogram has three peaks that respectively represent the
background, the upper part and the nether part of the glass. And it is compact
and only occupies a fraction of the whole gray levels.
-
8/4/2019 Summer Training Baljeet
25/28
So the image enhanced by self plateau histogram equalization is better than the
image enhanced by equalization histogram.
It is compact and only occupies a fraction of the whole gray space. Fig(b) is the
enhanced image by histogram equalization. The ship is too brightened the
background of the sea surface and the sky are greatly enhanced. And they made
one uncomfortable. Fig. (e) is the histogram of image (b). In Fig. (e), the first
peak correspond to back-grounds occupy approximately the whole gray levels,
and the last peak in Fig. (c) disappeared in Fig. (e). So the backgrounds are
mainly enhanced by histogram equalization .Fig. (c) is the enhanced image by
self-adaptive plateau histogram equalization.
-
8/4/2019 Summer Training Baljeet
26/28
Conclusion
Infrared images can be enhanced effectively by the proposed algorithm. It
has advantages over histogram equalization. And the plateau threshold value
can be self-adaptive selected in this algorithm. By using pipeline and parallel
computation architecture, the system can process25 frames of 128 128 8 bits
-
8/4/2019 Summer Training Baljeet
27/28
infrared images in every second. And the experimental results show that the
quality of enhanced image by self-adaptive plateau histogram equalization is
better than the quality of enhanced image by histogram equalization. It works
well in the infrared image process system.
REFERENCES
Image processing using MATLAB by C.Gonzalez
-
8/4/2019 Summer Training Baljeet
28/28
Electro optical tracking systems considerationsA.George
Downey, SPIE volume 1111, Acquisition, tracking and pointing
part 3rd .
www.wikipedia.com
http://www.octec.co.uk/index.html
http://kdl.cs.umass.edu/prox_overview/documentation/tutorial/in
dex.html
www.sciencedirect.co.in
http://www.wikipedia.com/http://www.octec.co.uk/index.htmlhttp://kdl.cs.umass.edu/prox_overview/documentation/tutorial/index.htmlhttp://kdl.cs.umass.edu/prox_overview/documentation/tutorial/index.htmlhttp://www.wikipedia.com/http://www.octec.co.uk/index.htmlhttp://kdl.cs.umass.edu/prox_overview/documentation/tutorial/index.htmlhttp://kdl.cs.umass.edu/prox_overview/documentation/tutorial/index.html