final report on the major research project...
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FINAL REPORT ON THE MAJOR RESEARCH PROJECT
DEVELOPMENT OF EFFICIENT TECHNIQUES FOR
FEATURE EXTRACTION AND CLASSIFICATION
FOR INVARIANT PATTERN MATCHING AND
COMPUTER VISION APPLICATIONS
DURATION: 01-07-2015 TO 30-06-2018
Submitted to:
UNIVERSITY GRANTS COMMISSION, NEW DELHI
by
Dr. Chandan Singh
Professor (Re-employed)
Department of Computer Science
Punjabi University, Patiala
July 2018
Annexure -VIII
FINAL REPORT ON THE PROJECT
TITLE: DEVELOPMENT OF EFFICIENT TECHNIQUES FOR FEATURE
EXTRACTION AND CLASSIFICATION FOR INVARIANT PATTERN
MATCHING AND COMPUTER VISION APPLICATIONS.
1. Project Report No. 1st /2nd /3rd/Final Final Report
2. UGC Reference No. F. F. No.-43-275/2014(SR)
3. Period of Report From 01-07-2015 to 30-6-2018
4. Title of the Research Project Development of Efficient Techniques for Feature
Extraction and Classification for Invariant Pattern
Matching and Computer Vision Applications.
5. a. Name of the Principal Investigator
b. Deptt
c. University/College where work has
progressed
Dr. Chandan Singh
Department of Computer Science.
Punjabi University, Patiala-147002, Punjab
6. Effective Date of Starting of the Project 01-07-2015
7. Grant Approved and Expenditure Incurred
During the Period of the Report
a. Total Amount Approved (Rs.)
b. Total Expenditure (Rs.)
c. Report of the Work Done
i. Brief Objective of the Project.
Rs. 13,70,000/-
Rs. 11,46,560/-
Objective: The objective of the proposed research work is to develop
effective techniques for pattern recognition using
orthogonal radial invariant moments (ORIMs) by
enhancing their accuracy, numerical stability and reducing
their speed of computation. Instead of using only
magnitude of ORIMs as features, complex ORIMs using
both the magnitude and phase will be used for feature
detection. These features will then be combined with better
classifiers, such as the SVM and ANN, for enhancing the
recognition rate. Keeping in view the requirements of
various applications with regard to recognition rate and
processing speed, optimal solutions for these applications
will be provided. The scope of the research work will also
be extended to orthogonal radial invariant transforms
(ORITs) which have characteristics similar to ORIMs but
possess less time complexity.
ii. Work done so far and results
achieved and publications, if
any, resulting from the work
(Give details of the papers and
names of the journals in which
it has been published or
accepted for publication.
iii. Has the progress been
accordingly to original plan of
work and towards achieving
objectives if not, state reasons.
iv. Please indicate the difficulties,
if any, experienced in
implementing the project
v. If project has not been
completed, please indicate the
approximate time by which it is
likely to be completed. A
summary of the work done for
the period (annual basis) may
please be sent to the
commission on a separate sheet.
vi. If the project has been
completed, please enclose a
summary of the findings of the
study. One bound copy of the
final report of work done may
also be sent to University
Grants Commission.
Please Refer Appendix-A
Yes, the progress of the project is as per plan.
None
Not Applicable, as the project has been completed.
Summary of the Findings: (Please Refer Appendix-A)
The object matching and classification is a classical
problem in digital image processing which has several
applications in real-life problems such as image retrieval,
face recognition, biometric recognition, surveillance,
optical character recognition, image super-resolution,
medical image segmentation, noise removal, etc. The
process of object matching depends heavily on feature
extraction to represent an image effectively. The features
should be invariant to geometric and photometric
distortions. Earlier, these tasks were performed on the
grayscale images. Nowadays, the grayscale images are
being replaced by color images for these tasks.
To address these issues we have developed several
effective descriptors for gray-scale and color images.
These descriptors have been tested with various
unsupervised and supervised classifiers. Effective systems
have been developed for the task of object recognition and
scene classification, optical character recognition, noise
removal in medical images, brain MRI segmentation, and
image super-resolution. The systems are based on well-
vii. Any other information which
would help in evaluation of
work done on the project. At
the completion of the project,
the first report should indicate
the output, such as (a)
Manpower trained (b) Ph.D.
awarded (c) Publication of
results (d) other impact, if any
defined theories supported by detailed experimental
analysis. Ten research papers have been published in
international journal of repute with high Thomson Reuters
Impact Factor. Out of the ten research papers, five papers
are directly related to the project and the other five are
closely related to it.
a. Manpower Trained: Twelve M.Tech (CSE)
students have worked for their dissertation in the
areas closely related to the scope of the project.
b. Ph.D. Awarded: Two research scholars Mr.
Ashutosh Aggarwal, and Mr. Karamjeet Singh ,
have completed their Ph.D. degree, and one
research scholar, Ms. Kanwalpreet Kaur, have
submitted her Ph.D. thesis on the topics closely
related to the project. Ms. Anu Bala, Project
Fellow, is also working for her Ph.D. in this area.
c. Publication of Results: 10 research papers have
been published in the leading journals with high
Thomson Reuters Impact Factors, and 7 papers
have been communicated for their publication.
d. Other Impact, if Any: The findings related to the
proposed multi-channel orthogonal rotation
invariant moments for the representation of color
objects is likely to impact the research activities in
pattern recognition and computer vision
applications.
Annexure -IX
INFORMATION ON THE MAJOR RESEARCH PROJECT
TITLE: DEVELOPMENT OF EFFICIENT TECHNIQUES FOR FEATURE
EXTRACTION AND CLASSIFICATION FOR INVARIANT PATTERN
MATCHING AND COMPUTER VISION APPLICATIONS.
1. Title of the Project Development of Efficient Techniques for Feature Extraction and
Classification for Invariant Pattern Matching and Computer Vision
Applications.
2. Name and Address of the
Principal Investigator
Dr. Chandan Singh
Address:
Office: Professor(Re-employed),
Department of Computer Science, Punjabi University, Patiala-147002,
Punjab
M : 9872043209
Residential: H. N. 82, Urban Estate, Phase-3, Patiala-147002, Punjab
3. Name and Address of the
Institution
Department of Computer Science, Punjabi University, Patiala-147002,
Punjab
4. UGC Approval Letter No. & Date F. No.-43-275/2014(SR)
5. Date of Implementation 01-07-2015
6. Tenure of the Project 3 years, from 01-07-2015 to 30-06-2018
7. Total Grant Allocated Total Allocation Rs. 13,70,000/-
8. Total Grant Received Total Received Rs.11,05,800/-
9. Final Expenditure Total Expenditure Rs. 11,46,560/-
10. Title of the Project Development of Efficient Techniques for Feature Extraction and
Classification for Invariant Pattern Matching and Computer Vision
Applications.
11. Objectives of the Project Objective: The objective of the proposed research work is to develop effective
techniques for pattern recognition using orthogonal radial invariant
moments (ORIMs) by enhancing their accuracy, numerical stability and
reducing their speed of computation. Instead of using only magnitude of
ORIMs as features, complex ORIMs using both the magnitude and
phase will be used for feature detection. These features will then be
combined with better classifiers, such as the SVM and ANN, for
enhancing the recognition rate. Keeping in view the requirements of
various applications with regard to recognition rate and processing
speed, optimal solutions for these applications will be provided. The
scope of the research work will also be extended to orthogonal radial
invariant transforms (ORITs) which have characteristics similar to
ORIMs but possess less time complexity.
12. Whether Objectives were
Achieved (Give Details)
Refer Appendix-B
13. Achievements from the Project There are five major achievements:
i. We have developed a fast procedure for the computation of radial
moments which will help in the use of the moments in several
image processing applications. This is applicable to radial
transforms also.
ii. We have developed a multi-channel framework for the
computation of radial moments for the recognition and
classification of color objects and established its superiority over
the quaternion moments in these tasks. The quaternion moments
have been developed recently by several researchers and these
researchers have been claiming superiority of their quaternion
moments over multi-channel moments.
iii. Quaternion generalized Chebyshev-Fourier and quaternion
pseudo-Jacobi-Fourier moments for the color object recognition
have been developed. Optimal values of the free parameter
attributed to their generalization have been obtained to yield
superior object recognition performance.
iv. A fusion of multi-channel Zernike moments, Zernike moments of
magnitude of the gradient image, and color histograms has been
proposed which provides very high recognition rates under
geometric and photometric distortions of images using multi-
kernel learning SVM as classifier.
v. The rotation invariant moments have been applied in the
problems of optical character recognition (OCR), image super-
resolution, noise removal in the medical image, brain MRI
segmentation under noisy conditions, etc.
The publication of ten research papers (five related directly to the
objectives of the project and five closely related to the project) in
international journals of high repute with high Thomson Reuters Impact
Factor is a testimony to the high level of achievements of the project.
14. Summary of the findings
(In 500 words)
The object matching and classification is a classical problem in digital
image processing which has several applications in real-life problems
such as image retrieval, face recognition, biometric recognition,
surveillance, optical character recognition, image super-resolution,
medical image segmentation, noise removal, etc. The process of object
matching depends heavily on feature extraction to represent an image
effectively. The features should be invariant to geometric and
photometric distortions. Earlier, these tasks were performed on the gray
scale images. Now-a-days, the gray scale images are being replaced by
color images for these tasks.
To address these issues we have developed several effective
descriptors for gray-scale and color images. These descriptors have been
tested with various unsupervised and supervised classifiers. Effective
systems have been developed for the task of object recognition and
scene classification, optical character recognition, noise removal in
medical images, brain MRI segmentation, and image super-resolution.
The systems are based on well-defined theories supported by detailed
experimental analysis. Ten research papers have been published in
international journal of repute with high Thomson Reuters Impact
Factor. Out of the ten research papers, five papers are directly related to
the project and the other five are closely related to it.
15. Contribution to the Society
(Give Details)
The methods developed under the project have direct applications to
many practical problems in the areas of digital image processing and
computer vision. These include image retrieval, face recognition,
biometric recognition, surveillance, optical character recognition, brain
MRI segmentation for better brain disease diagnosis, image super-
resolution, noise removal in medical images, etc. The computer
scientists working in these areas can use the approaches for improving
the performance of the existing systems as these methods have been
published in reputed international journals with high Thomson Reuters
Impact Factors.
16. Whether any Ph.D Entrolled/
Produced out of the Project.
A Project Fellow, Ms Anu Bala, is working under this project who is
also working for her Ph.D. She is a co-author of a published paper. Two
more Ph.D. scholars, Mr. Jaspreet Singh and Mr. Shahbaz Mazeed, are
also working on problems related to the project. Mr. Jaspreet Singh is a
co-author of two published papers under the project. In addition, two
research scholars, Dr. Ashutosh Aggarwal and Dr. Karamjeet Singh,
have also worked for their Ph.D. degrees whose topics are closely
related to the topic of the project.
17. No. of Publications out of the
Project.
(Please Attach)
No. of publications directly related to the project: 05
No. of publications closely related to the project: 05
No. of papers communicated: 07
For the List of Publications, Please Refer Appendix-C
Appendix-A
ITEM 7(c) (ii): WORK DONE SO FAR AND RESULTS ACHIEVED AND
PUBLICATIONS RESULTING FROM THE WORK
Summary of the Work Done:
1. We have developed multi-channel orthogonal rotation invariant moments (MORIMs)
features for representing color images. The ORIMs descriptors have been derived by
concatenating the ORIMs features of each channel of a color image, i.e., ORIMs features
of R-, G-, and B-component of a color image. The performance of MORIMs descriptors
has been compared with that of recently developed quaternion ORIMs (QORIMs) of
color images using quaternion algebra. We have observed that the MORIMs features
outperform QORIMs features in the task of image retrieval, object recognition and scene
classification. This is a significant finding because the researchers of QORIMs
approaches have been claiming the superiority of QORIMs over MORIMs. We have
published these findings in a recent issue of a leading journal, Digital Signal Processing.
Another class of important feature descriptors for gray scale images belongs to image
moments based on generalized Chebyshev-Fourier moments (G-CHFMs) and generalized
pseudo-Jacobi-Fourier moments (G-PJFMs). These moments are characterized by a
parameter α which provides a generalized form of these ORIMs to select the moment for
image representation to yield its best performance. We have extended them to derive their
quaternion forms to yield two descriptors QG-CHFMs and QG-PJFMs to represent color
images and have obtained the optimum values of α which provide the best recognition
rates for object recognition and best accuracy for scene classification problems. A paper
has been published in a recent issue of a leading journal, Optics and Laser Technology.
2. Many objects undergo various geometric transformations such as translation, rotation,
and scale. During the acquisition process, images of objects can be affected by noise. The
ORIMs and (orthogonal rotation invariant transforms) ORITs provide very effective
descriptors for geometric and photometric changes in the image. To provide more
effective solutions, we have investigated the ZMs of gradient images, called GZMs and
fused them with ZMs of the images. These concepts have been extended to color images.
The color histograms (CH), which are rotation and scale invariant, are used as color
features. Among the various combinations of the color (CH), shape (MZMs), and texture
(GZMs), we have observed that the combination MZMs+GZMs+CH provides very high
recognition rates under the geometric and photometric changes in the image. It is
observed that when multi-modality features are used, the multiple-kernel learning-based
SVM (MKL-SVM) provides very high recognition rate as compared to their individual
performance. Various studies have shown that when it comes to the fusion of features
from different modalities, then the SVM performs better than the ANN. Therefore, in this
report, we have not studied ANN. A paper on this topic has been sent for publication.
3. The ORIMs suffer from high computation complexity. We have reduced the computation
complexity by developing a new convolution model. This convolution model is used in
all applications that we have considered under this project. An extensive study of
distance-based classifiers and SVM-based classifiers is made. It is observed that the
SVM-based classifiers respond differently with RBF and pre-computed kernels and in
many applications the pre-computed kernels provide better results.
4. We have worked on ORITMs features and observed that their features provide similar
performance to those of the ORIMs. They have been extended to quaternion forms and
have been analyzed for their response to all applications which have been considered for
ORIMs. They provide speed advantage over ORIMs. Their computation can be made
faster by using the convolution model developed by us for the ZMs. Keeping in view the
similarities of the ORIMs descriptors with the ORITMs features in respect to their
computational aspects and descriptors performance, we are not providing the details of
the analysis of the ORITs.
5. The ORIMs-based features for the grayscale images and the MORIMs-based features
developed under the project have been applied in many practical problems, viz, optical
character recognition (OCR) of Gurumukhi script, image retrieval, object recognition,
scene classification, denoising of images sequence, noise removal in medical images,
brain MR image segmentation, image up-sampling, and image super-resolution.
Already 10 papers have been published in international journals of high repute with good
Thomson Reuters Impact Factors. Moreover, 7 papers have been communicated for
publication.
List of Publications(Published/Communicated) Under the Project:
1. Chandan Singh, Ashutosh Aggarwal, Sukhjeet Kaur, A New Convolution Model for the
Fast Computation of Zernike Moments, International Journal of Electronics and
Communications, 72(2017)104-113,https://doi.org/10.1016/j.aeue.2016.11.014 Thomson
Reuters Impact Factor: 2.115, Publisher: ELSEVIER.
2. Ashutosh Aggarwal, Chandan Singh, Zernike Moments-Based Gurumukhi Character
Recognition, Applied Artificial Intelligence, 30(5) (2016)429-444, Publisher: Taylor &
Francis https://doi.org/10.1080/08839514.2016.1185859 Thomson Reuters Impact
Factor: 0.50.
3. Chandan Singh, Ashutosh Aggarwal, Single Image Super-Resolution Using Orthogonal
Rotation Invariant Moments, Computers and Electrical Engineering, 62(2017)266-280.
Publisher: ELSEVIER. Thomson Reuters Impact Factor: 1.747.
https://doi.org/10.1016/j.compeleceng.2017.02.009.
4. Chandan Singh, Jaspreet Singh, Multi-Channel Versus Quaternion Orthogonal Rotation
Invariant Moments for Color Image Representation, Digital Signal Processing
78(2018)376-392. Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.241
https://doi.org/10.1016/j.dsp.2018.04.001.
5. Chandan Singh, Jaspreet Singh, Quaternion generalized Chebyshev-Fourier and pseudo-
Jacobi-Fourier Moments for Color Object Recognition, Optics and Laser Technology,
106 (2018), 234-250 Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.503
https://doi.org/10.1016/j.optlastec.2018.03.033.
6. Chandan Singh, Ashutosh Aggarwal, An Effective Approach for Noise Robust and
Rotation Invariant Handwritten Character Recognition Using Zernike Moments Features
and Optimal Similarity Measure (Communicated).
7. Chandan Singh, Anu Bala, A Local Zernike Moment-based Non-Local Fuzzy C-Means
Algorithm for Segmentation of Brain Magnetic Resonance Images (Communicated).
8. Chandan Singh, Anu Bala, An Effective Local Zernike Moment-based Based Fuzzy C-
Means Algorithm Using Nonlocal Information for Segmentation of Brain Magnetic
Resonance Images (Communicated).
9. Chandan Singh, Jaspreet Singh, Robustness of Multi-Channel and Quaternion Orthogonal
Circularly Invariant Moments for Object Recognition and Scene Classification Using
Support Vector Machine (Communicated).
10. Chandan Singh, Jaspreet Singh, Geometrically Invariant Color, Shape and Texture
Features for Object Recognition Using Multiple Kernel Learning Classification Approach
(Communicated).
List of Publications Closely Related to the Area of the Project:
1. Chandan Singh, Ashutosh Aggarwal, An Efficient Approach for Image Sequence
Denoising Using Zernike Moments-Based Nonlocal Means Approach, Computers
&Electrical Engineering, (2015) https://doi.org/10.1016/j.compeleceng.2015.09.006 (SCI
Indexed), Thomson Reuters Impact Factor: 1.747. Publisher: ELSEVIER. ISSN No.
0045-7906.
2. Chandan Singh, Ashutosh Aggarwal, A Comparative Performance Analysis of DCT-
Based and Zernike Moments-Based Image Up-Sampling Techniques, Optik,
127(4)(2016)2158-2168 (SCI Indexed), https://doi.org/10.1016/j.ijleo.2015.11.115
Thomson Reuters Impact Factor: 1.191. Publisher: ELSEVIER. ISSN No. 0030-4026.
3. Chandan Singh, Sukhjeet Kaur, Karamjeet Singh, Invariant Moments and Transform-Based
Unbiased Nonlocal Means for Denoising of MR Images, Biomedical Signal Processing
and Control, 30(2016)13-24, https://doi.org/10.1016/j.bspc.2016.05.007, Thomson
Reuters Impact Factor: 2.783, Publisher: ELSEVIER. ISSN No. 0030-4026.
4. Chandan Singh, Ekta Walia, Kanwal Preet Kaur, "Enhancing color image retrieval
performance with feature fusion and non-linear support vector machine classifier" in the
journal "IJLEO", Optik, 158(2018)127-141, https://doi.org/10.1016/j.ijleo.2017.11.202,
Publisher: ELSEVIER. Thomson Reuters Impact Factor: 1.191.
5. Chandan Singh, Anu Bala, A DCT-based Local and Non-Local Fuzzy C-means Algorithm
for Segmentation of Brain Magnetic Resonance Images, Applied Soft Computing,
68(2018)447-457, Publisher: ELSEVIER. https://doi.org/10.1016/j.asoc.2018.03.054
Thomson Reuters Impact Factor: 3.907.
6. Chandan Singh, Shahbaz Majeed, Face Recognition Using Gabor and Local Binary
pattern Features of Color Images with Opponent Color Models (Communicated).
7. Chandan Singh, Shahbaz Majeed, Robust Color Texture Descriptors for Color Face
Recognition (Communicated).
Appendix-B
ITEM-12: DETAILS OF THE OBJECTIVES ACHIEVED
We present here the details of the objectives achieved.
Development of Effective Features: The rotation invariant moments (RIMs) and transforms
such as Zernike moments (ZMs), pseudo-Zernike moments (PZMs), angular radial transforms
(ARTs) have been studied extensively and rotation invariant features have been derived.
Recently developed quaternion moments have also been investigated. A class of RIMs which
consists of orthogonal moments is called orthogonal rotation invariant moments (ORIMs). The
RIMs and ORIMs have been implemented for the representation of color images. The angular
radial transform (ART) has been found to be very useful when rotation invariant features are
used and fast computation of features is required. A comparative performance analysis of the
ART over ZMs has been performed and it is observed that ART provides competitive
performance over ZMs but with very time efficient manner. ZMs and their phase angles have
also been investigated for invariant OCR applications. Detailed experiments carried out on
Wang, Corel, OT-Scene, COIL-100, and ALOI datasets confirm the superiority of ZMs features
over the QZMs using various distance measures.
We have developed multi-channel orthogonal rotation invariant moments (MORIMs) features for
representing color images. The ORIMs descriptors have been derived by concatenating the
ORIMs features of each channel of a color image, i.e., ORIMs features of R-, G-, and B-
component of a color image. The performance of MORIMs descriptors has been compared with
that of recently developed quaternion ORIMs (QORIMs) of color images using quaternion
algebra. We have observed that the MORIMs features outperform QORIMs features in the task
of image retrieval, object recognition and scene classification. This is a significant finding
because the researchers of QORIMs approaches have been claiming the superiority of QORIMs
over MORIMs. We have published these findings in a recent issue of a leading journal, Digital
Signal Processing.
Another class of important feature descriptors for gray scale images belongs to image moments
based on generalized Chebyshev-Fourier moments (G-CHFMs) and generalized pseudo-Jacobi-
Fourier moments (G-PJFMs). These moments are characterized by a parameter α which provides
a generalized form of these ORIMs to select the moment for image representation to yield its
best performance. We have extended them to derive their quaternion forms to yield two
descriptors QG-CHFMs and QG-PJFMs to represent color images and have obtained the
optimum values of α which provides the best recognition rates for object recognition and best
accuracy for scene classification problems. A paper has been published in a recent issue of a
leading journal, Optics and Laser Technology.
The procedure for the feature extraction and classification using orthogonal rotation invariant
transforms (ORITs) is similar to that of ORIMs both for the grayscale and color images. Their
descriptive performance is not much significantly different from the ORIMs. Therefore, we do
not present their details in this report.
Development of Effective Classifiers: We have performed experiments using various distance-
based classifiers such as 𝐿1-norm, 𝐿2-norm, Canberra, extended Canberra, 𝜒2, square-chord, and
histogram intersection. The distance-based classifiers are faster than the training based classifiers
such as ANN and SVM. A number of studies have shown that SVM is more powerful than the
ANN for object recognition, where each image is represented by multiple set of features and the
task of the recognition is performed on the combined features. Therefore, we have not studied
ANN in the present study. Further, we have applied the distance measures in various applications
and observed that 𝐿2-norm performs very well for all moments and transforms. The performance
of non-linear SVM classifiers as proposed by us provides very high recognition rates. To achieve
these goals, a framework has been developed which enhances the performance of color image
retrieval system using the non-linear SVM classifiers: For this purpose, we have suggested a new
scheme for the fusion of various color, texture and shape features in order to provide effective
contribution of each type of features. A non-linear classifier using the Gaussian kernel of
Canberra, 𝜒2, square-chord, and extended Canberra distance based kernels has been used for the
problem of image retrieval. It is observed that the proposed classifier provides much superior
results as compared to the existing radial basis function (RBF) based SVM and linear SVM
classifiers.
The SVM classifier has further been used to analyze the performance of ORIMs features of color
images for the tasks of object recognition and scene classification. As discussed earlier, the
MORIMs features provide better performance than the QORIMs features in the tasks of image
retrieval, object recognition and scene classification of color images. These observations have
been made using the classical distance-based classifiers. Next, we use SVM classifiers for these
tasks with the classical linear and RBF kernels as well as the pre-computed kernels based on
Canberra, extended Canberra, 𝜒2, square-chord, and histogram intersection-based basis
functions. We have observed that the SVM classifiers based on the classical kernel functions as
well as the proposed pre-computed kernels provide much more improvement in performance of
MORIMs features than improvement in the QORIMs feature.
Fusion of Features for Enhancing Recognition Rate: When we fuse one type of features with
the others, it is expected that the recognition rate will improve. However, fusion of the features is
not an easy task because it may deteriorate the results as compared to the performance of the
independent feature sets. Therefore, normalization of the features before their fusion is suggested
in our work. The individual features are normalized by the average of a feature component which
is obtained by considering the value of that component for all database images. This approach
has provided very good results in many experiments. In fact, our proposed approach for non-
linear SVM is based on the normalization of the features before applying the Gaussian kernel of
the SVM.
The multiple kernel learning (MKL) is one of the most popular methods used in computer vision
to linearly combine the similarity functions between images to yield the improved classification
performance. We develop a framework to derive and combine features from three different
modalities which represent object color, shape and texture. The MKL method is used to combine
the three feature cues to maximize the object recognition and classification performance. We use
the MZMs features for the low-level shape representation of color object. To represent high-level
shape information, we use the gradient of a color image which yields texture of the image. For
this purpose, we derive the gradient magnitude image of a color image and derive the ZMs of the
gradient image (GZMs). The color information is represented by the color histogram (CH) which
provides very simple but effective color information which is invariant to image rotation and
scale. Finally, the MKL approach is used for the classification task, which yields very high
recognition rate under normal condition as well as under rotation and scale.
The following research papers have been published or are under publication out of the work
carried out under this project:
1. Fast Algorithms for the Computation of Moments and Transforms: We have
developed a fast convolution model for the computation of moments and transforms. The
proposed approach is general as it can be used with any moment and transform which are
rotation invariant and possess 8-way symmetry in their radial kernel functions and 8-way
anti-symmetry in their angular radial functions. Both moments and transforms under our
investigation possess these properties. We have published a research paper [1] for
ZMs. This technique is very useful for many image processing applications such as
image denoising, image super-resolution, medical image segmentation and image
retrieval.
2. Zernike Moments-Based Gurumukhi Character Recognition: Invariant ZMs have
been applied for the recognition of optical character recognition (OCR) of Gurumukhi
script. It is shown that the ZMs features, which are rotation and scale invariant, are very
useful for recognizing Gurumukhi characters which are in any orientation and have
arbitrary size. Two computational frameworks have been proposed: inner unit disk and
outer unit disk models. Two classifiers are used: 𝐿2-norm and SVM.
Experimental results demonstrate that the outer unit disk model provides better results
than the inner unit disk model. Also, further enhancement in Gurumukhi character
recognition can be achieved with help of SVM instead of using 𝐿2-norm. A paper
related to this work has been published [2].
3. Orthogonal Rotation Invariant Moments in Single Image Super-Resolution: Here,
we propose an interpolation-based single-frame image super-resolution(SR) approach
using orthogonal rotation invariant moments (ORIMs). Among the various ORIMs,
Zernike moments (ZMs), pseudo-Zernike moments (PZMs) and orthogonal Fourier
Mellin moments (OFMMs) have been considered in our proposed framework. The SR
performance of the proposed approach has been compared with the classical
interpolation-based approaches like bicubic, cubic B-spline, and Lanczos, as well as with
nonlocal-means (NLM), and recently developed NLM+ZMs and NLM+PZMs-based SR
approaches on twelve standard test images. The results demonstrate the superiority of the
proposed ORIMs-based approach in super-resolving both noise-free and noisy images
over recently developed NLM+ORIMs-based SR approaches. Also, a comparative
performance analysis, among various ORIMs (ZMs, PZMs, and OFMMs), is also
presented to determine that ORIM which performs better over others under various
conditions. A time complexity analysis shows that the proposed method is very fast as
compared to NLM, NLM+ZMs and NLM+PZMs-based methods. A paper pertaining to
this work has been published [3].
4. Multi-Channel Versus Quaternion Orthogonal Rotation Invariant Moments for
Color Image Representation: Orthogonal rotation invariant moments (ORIMs) have
been used in many pattern recognition and image processing applications in the last three
decades. Most of the applications relate to monochrome and gray-scale images. Recently,
the theory of image moments for gray-scale images has been extended to color images
using quaternion moments to explore the benefit of color information while representing
the color images by moments. We have proposed multi-channel ORIMs (MORIMs)
invariants for color images and analyze them by multi-channel moments and the existing
quaternion moments, called quaternion orthogonal rotation invariant moments
(QORIMs). The theoretical and experimental analysis demonstrates the superiority of the
proposed MORIMs over the QORIMs invariants in the color image recognition task. The
experiments are conducted by considering Zernike moments (ZMs) and quaternion ZMs
(QZMs) as the representatives of MORIMs and QORIMs, respectively. A paper
pertaining to this work has been published [4].
5. Quaternion Generalized Chebyshev-Fourier and Pseudo-Jacobi-Fourier Moments
for Color Object Recognition: The classical generalized Chebyshev-Fourier (G-
CHFMs) and generalized pseudo-Jacobi-Fourier moments (G-PJFMs) have been
extended to represent color images using quaternion algebra. The proposed quaternion G-
CHFMs (QG-CHFMs) and quaternion G-PJFMs (QG-PJFMs) are characterized by a
parameter 𝛼, called free parameter,which distinguishes them from the conventional
Chebyshev-Fourier moments (CHFMs) and pseudo-Jacobi-Fourier moments (PJFMs).
All these moments are rotation-invariant and orthogonal. The effect of the parameter 𝛼
on image reconstruction and object recognition is studied in detail and its optimal values
have been obtained for these two image processing tasks. It is shown that the choice of 𝛼
influences significantly the image reconstruction capability and the object recognition
performance of the proposed QG-CHFMs and QG-PJFMs moments. Extensive
experiments are conducted to demonstrate the behavior of these moments on image
reconstruction and object recognition under normal condition and under rotation, scaling,
and noise using COIL-100, SIMPLIcity and Coreldatasets of color objects. A paper
pertaining to this work has been published [5].
6. An Effective Approach for Noise Robust and Rotation Invariant Handwritten
Character Recognition using Zernike Moments Features and Optimal Similarity
Measure: The approach used in this paper uses a new optimal similarity measure which
uses both the magnitude and phase angle of the ZMs. This approach provides better
recognition rate compared to the conventional magnitude based similarity measure. The
approach has been applied on three datasets: MNIST (numerals in Roman), GurChar
(Gurumukhi Characters) and GurNum (Gurumukhi numerals). The proposed method and
classifier outperform the ZMs magnitude based features and various distance classifiers
including the support vector machine (SVM) classifier. It has been observed that the
proposed approach provides much superior recognition results over the state-of-the-art
methods. A paper on this work has been sent for publication [6].
7. A Local Zernike Moment-based Non-Local Fuzzy C-Means Algorithm for
Segmentation of Brain Magnetic Resonance Images: Magnetic resonance (MR)
images are often corrupted with Rician noise and are affected by intensity in-
homogeneity. The existing methods dealing with such issues and using the nonlocal and
local information work in the spatial domain. We have proposed a method which works
in the moment domain. The proposed method effectively deals with the Rician noise and
intensity in-homogeneity. We select local Zernike moments (LZMs) for the moment-
based approach because it possesses better pattern matching capability under geometric
and photometric distortions in the images. We develop a framework which uses two
stages. In the first stage, we denoise the MR image using LZMs to remove the Rician
noise. In the second stage, the original image in conjunction with the Rician-noise-free
image is used for the nonlocal and local information for the segmentation process.
Detailed experimental results are provided to demonstrate the superior performance of
the proposed method over the existing state-of-the-art methods. A paper on this work
has been sent for publication [7].
8. An Effective Local Zernike Moment-based Based Fuzzy C-Means Algorithm Using
Nonlocal Information for Segmentation of Brain Magnetic Resonance Images: Brain
MR images (MRIs) suffer from many artifacts such as noise and intensity non-
uniformity. Moreover, they contain an abundant amount of fine image structures, edges,
and corners. These anomalies affect the segmentation process of the brain MRIs which is
required by physicians for the diagnosis purpose. Recently, the nonlocal fuzzy-based
segmentation approaches have dealt these issues in the intensity domain using the
nonlocal approach. The main concept of these studies is the use of image redundancy
about the local neighborhood of a pixel in the wider region called the nonlocal
neighborhood. The redundancy is searched in the intensity domain. In this paper, we
search the redundancy in the moment domain using the local Zernike moments (LZMs).
The LZMs are robust to the various anomalies that afflict the brain MR images as
discussed in the paper. We develop a framework based on the LZMs for the segmentation
of brain MR images and demonstrate how the redundancy is addressed in the moment
domain to provide better segmentation accuracy. Experimental results on both simulated
and real MR images show the superiority of the proposed method in terms of accuracy
and robustness to image noise and intensity non-uniformity as compared to the state-of-
the-art approaches. A paper on this work has been sent for publication [8].
9. Robustness of Multi-Channel and Quaternion Orthogonal Circularly Invariant
Moments for Object Recognition and Scene Classification Using Support Vector
Machine: In the last few decades, orthogonal circularly invariant moments have been
used in many computer vision and pattern recognition applications. In the recent past, the
orthogonal moments for gray-scale image have been extended to quaternion moments to
explore the benefits by representing a color image in a holistic manner. In this paper, we
analyze from various aspects the color image representation capability of multi-channel
orthogonal rotation invariant moments (MORIMs) and quaternion orthogonal rotation
invariant moments (QORIMs). Extensive experiments are conducted under various
geometric changes in the context of distance-based similarity measures and kernel-based
support vector machine (SVM). The experiments are conducted on Zernike moments
(ZMs) which is considered as the representative of MORIMs and QORIMs on COIL-100,
SIMPLIcity, ALOI, and OT-scene datasets. A paper on this work has been sent for
publication [9].
10. Geometrically Invariant Color, Shape and Texture Features for Object Recognition
Using Multiple Kernel Learning Classification Approach
We have developed a framework for the fusion of the geometrically invariant descriptors
representing color, shape and texture for the recognition of color objects using the
multiple kernel learning (MKL) approach. We propose an effective rotation invariant
texture descriptor which is based on the Zernike moments (ZMs) of the gradient of the
color images, referred to as the GZMs. For the shape features of the color objects, we use
the ZMs of the intensity component of a color image and also mulit-channel ZMs
(MZMs) which have proven to be superior in performance than the quaternion ZMs
(QZMs). For the purpose of comparative performance analysis, rotation invariants of the
QZMs (RQZMs) are also considered. The color histograms (CH) are known to be very
effective color descriptors. The five sets of features – CH, ZMs, GZM, MZMs, and
RQZMs are invariant to translation, rotation, and scale. The fusion of the color, shape and
texture features in different combinations using the MKL approach is shown to provide
very high recognition rates on PASCAL VOC 2005, Soccer, SIMPLIcity, Flower, and
Caltech-101 datasets. A paper on this work has been sent for publication [10].
List of Publications(Published/Communicated) Under the Project:
1. Chandan Singh, Ashutosh Aggarwal, Sukhjeet Kaur, A New Convolution Model for the
Fast Computation of Zernike Moments, International Journal of Electronics and
Communications, 72(2017)104-113,https://doi.org/10.1016/j.aeue.2016.11.014 Thomson
Reuters Impact Factor: 2.115, Publisher: ELSEVIER.
2. Ashutosh Aggarwal, Chandan Singh, Zernike Moments-Based Gurumukhi Character
Recognition, Applied Artificial Intelligence, 30(5) (2016)429-444, Publisher: Taylor &
Francis https://doi.org/10.1080/08839514.2016.1185859 Thomson Reuters Impact
Factor: 0.50.
3. Chandan Singh, Ashutosh Aggarwal, Single Image Super-Resolution Using Orthogonal
Rotation Invariant Moments, Computers and Electrical Engineering, 62(2017)266-280.
Publisher: ELSEVIER. Thomson Reuters Impact Factor: 1.747.
https://doi.org/10.1016/j.compeleceng.2017.02.009.
4. Chandan Singh, Jaspreet Singh, Multi-Channel Versus Quaternion Orthogonal Rotation
Invariant Moments for Color Image Representation, Digital Signal Processing
78(2018)376-392. Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.241
https://doi.org/10.1016/j.dsp.2018.04.001.
5. Chandan Singh, Jaspreet Singh, Quaternion generalized Chebyshev-Fourier and pseudo-
Jacobi-Fourier Moments for Color Object Recognition, Optics and Laser Technology,
106 (2018), 234-250. Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.503
https://doi.org/10.1016/j.optlastec.2018.03.033.
6. Chandan Singh, Ashutosh Aggarwal, An Effective Approach for Noise Robust and
Rotation Invariant Handwritten Character Recognition Using Zernike Moments Features
and Optimal Similarity Measure (Communicated).
7. Chandan Singh, Anu Bala, A Local Zernike Moment-based Non-Local Fuzzy C-Means
Algorithm for Segmentation of Brain Magnetic Resonance Images (Communicated).
8. Chandan Singh, Anu Bala, An Effective Local Zernike Moment-based Based Fuzzy C-
Means Algorithm Using Nonlocal Information for Segmentation of Brain Magnetic
Resonance Images (Communicated).
9. Chandan Singh, Jaspreet Singh, Robustness of Multi-Channel and Quaternion Orthogonal
Circularly Invariant Moments for Object Recognition and Scene Classification Using
Support Vector Machine (Communicated).
10. Chandan Singh, Jaspreet Singh, Geometrically Invariant Color, Shape and Texture
Features for Object Recognition Using Multiple Kernel Learning Classification Approach
(Communicated).
List of Publications Closely Related to the Area of the Project:
1. Chandan Singh, Ashutosh Aggarwal, An Efficient Approach for Image Sequence
Denoising Using Zernike Moments-Based Nonlocal Means Approach, Computers
&Electrical Engineering, (2015) https://doi.org/10.1016/j.compeleceng.2015.09.006 (SCI
Indexed), Thomson Reuters Impact Factor: 1.747. Publisher: ELSEVIER. ISSN No.
0045-7906.
2. Chandan Singh, Ashutosh Aggarwal, A Comparative Performance Analysis of DCT-
Based and Zernike Moments-Based Image Up-Sampling Techniques, Optik,
127(4)(2016)2158-2168 (SCI Indexed), https://doi.org/10.1016/j.ijleo.2015.11.115
Thomson Reuters Impact Factor: 1.191. Publisher: ELSEVIER. ISSN No. 0030-4026.
3. Chandan Singh, Sukhjeet Kaur, Karamjeet Singh, Invariant Moments and Transform-Based
Unbiased Nonlocal Means for Denoising of MR Images, Biomedical Signal Processing
and Control, 30(2016)13-24, https://doi.org/10.1016/j.bspc.2016.05.007, Thomson
Reuters Impact Factor: 2.783, Publisher: ELSEVIER. ISSN No. 0030-4026.
4. Chandan Singh, Ekta Walia, Kanwal Preet Kaur, "Enhancing color image retrieval
performance with feature fusion and non-linear support vector machine classifier" in the
journal "IJLEO", Optik, 158(2018)127-141, https://doi.org/10.1016/j.ijleo.2017.11.202,
Publisher: ELSEVIER. Thomson Reuters Impact Factor: 1.191.
5. Chandan Singh, Anu Bala, A DCT-based Local and Non-Local Fuzzy C-means Algorithm
for Segmentation of Brain Magnetic Resonance Images, Applied Soft Computing,
68(2018)447-457, Publisher: ELSEVIER. https://doi.org/10.1016/j.asoc.2018.03.054
Thomson Reuters Impact Factor: 3.907.
6. Chandan Singh, Shahbaz Majeed, Face Recognition Using Gabor and Local Binary
pattern Features of Color Images with Opponent Color Models (Communicated).
7. Chandan Singh, Shahbaz Majeed, Robust Color Texture Descriptors for Color Face
Recognition (Communicated).
Appendix-C
ITEM-20: DETAILS OF PUBLICATIONS RESULTING FROM THE PROJECT WORK
List of Publications(Published/Communicated) Under the Project:
1. Chandan Singh, Ashutosh Aggarwal, Sukhjeet Kaur, A New Convolution Model for the
Fast Computation of Zernike Moments, International Journal of Electronics and
Communications, 72(2017)104-113,https://doi.org/10.1016/j.aeue.2016.11.014 Thomson
Reuters Impact Factor: 2.115, Publisher: ELSEVIER.
2. Ashutosh Aggarwal, Chandan Singh, Zernike Moments-Based Gurumukhi Character
Recognition, Applied Artificial Intelligence, 30(5) (2016)429-444, Publisher: Taylor &
Francis https://doi.org/10.1080/08839514.2016.1185859 Thomson Reuters Impact
Factor: 0.50.
3. Chandan Singh, Ashutosh Aggarwal, Single Image Super-Resolution Using Orthogonal
Rotation Invariant Moments, Computers and Electrical Engineering, 62(2017)266-280.
Publisher: ELSEVIER. Thomson Reuters Impact Factor: 1.747.
https://doi.org/10.1016/j.compeleceng.2017.02.009.
4. Chandan Singh, Jaspreet Singh, Multi-Channel Versus Quaternion Orthogonal Rotation
Invariant Moments for Color Image Representation, Digital Signal Processing
78(2018)376-392. Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.241
https://doi.org/10.1016/j.dsp.2018.04.001.
5. Chandan Singh, Jaspreet Singh, Quaternion generalized Chebyshev-Fourier and pseudo-
Jacobi-Fourier Moments for Color Object Recognition, Optics and Laser Technology,
106 (2018), 234-250. Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.503
https://doi.org/10.1016/j.optlastec.2018.03.033.
6. Chandan Singh, Ashutosh Aggarwal, An Effective Approach for Noise Robust and
Rotation Invariant Handwritten Character Recognition Using Zernike Moments Features
and Optimal Similarity Measure (Communicated).
7. Chandan Singh, Anu Bala, A Local Zernike Moment-based Non-Local Fuzzy C-Means
Algorithm for Segmentation of Brain Magnetic Resonance Images (Communicated).
8. Chandan Singh, Anu Bala, An Effective Local Zernike Moment-based Based Fuzzy C-
Means Algorithm Using Nonlocal Information for Segmentation of Brain Magnetic
Resonance Images (Communicated).
9. Chandan Singh, Jaspreet Singh, Robustness of Multi-Channel and Quaternion Orthogonal
Circularly Invariant Moments for Object Recognition and Scene Classification Using
Support Vector Machine (Communicated).
10. Chandan Singh, Jaspreet Singh, Geometrically Invariant Color, Shape and Texture
Features for Object Recognition Using Multiple Kernel Learning Classification Approach
(Communicated).
List of Publications Closely Related to the Area of the Project:
1. Chandan Singh, Ashutosh Aggarwal, An Efficient Approach for Image Sequence
Denoising Using Zernike Moments-Based Nonlocal Means Approach, Computers
&Electrical Engineering, (2015) https://doi.org/10.1016/j.compeleceng.2015.09.006 (SCI
Indexed), Thomson Reuters Impact Factor: 1.747. Publisher: ELSEVIER. ISSN No.
0045-7906.
2. Chandan Singh, Ashutosh Aggarwal, A Comparative Performance Analysis of DCT-
Based and Zernike Moments-Based Image Up-Sampling Techniques, Optik,
127(4)(2016)2158-2168 (SCI Indexed), https://doi.org/10.1016/j.ijleo.2015.11.115
Thomson Reuters Impact Factor: 1.191. Publisher: ELSEVIER. ISSN No. 0030-
4026.
3. Chandan Singh, Sukhjeet Kaur, Karamjeet Singh, Invariant Moments and Transform-
Based Unbiased Nonlocal Means for Denoising of MR Images, Biomedical Signal
Processing and Control, 30(2016)13-24, https://doi.org/10.1016/j.bspc.2016.05.007,
Thomson Reuters Impact Factor: 2.783, Publisher: ELSEVIER. ISSN No. 0030-
4026.
4. Chandan Singh, Ekta Walia, Kanwal Preet Kaur, "Enhancing color image retrieval
performance with feature fusion and non-linear support vector machine classifier" in the
journal "IJLEO", Optik, 158(2018)127-141, https://doi.org/10.1016/j.ijleo.2017.11.202,
Publisher: ELSEVIER. Thomson Reuters Impact Factor: 1.191.
5. Chandan Singh, Anu Bala, A DCT-based Local and Non-Local Fuzzy C-means
Algorithm for Segmentation of Brain Magnetic Resonance Images, Applied Soft
Computing, 68(2018)447-457, https://doi.org/10.1016/j.asoc.2018.03.054 Publisher:
ELSEVIER. Thomson Reuters Impact Factor: 3.907.
6. Chandan Singh, Shahbaz Majeed, Face Recognition Using Gabor and Local Binary
pattern Features of Color Images with Opponent Color Models (Communicated).
7. Chandan Singh, Shahbaz Majeed, Robust Color Texture Descriptors for Color Face
Recognition (Communicated).