final report on the major research project...

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).

https://doi.org/10.1016/j.aeue.2016.11.014

https://doi.org/10.1080/08839514.2016.1185859

https://doi.org/10.1016/j.compeleceng.2017.02.009

https://doi.org/10.1016/j.dsp.2018.04.001

https://doi.org/10.1016/j.optlastec.2018.03.033

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,


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).


https://doi.org/10.1016/j.ijleo.2015.11.115

https://doi.org/10.1016/j.bspc.2016.05.007


https://doi.org/10.1016/j.asoc.2018.03.054

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].










Factor: 0.50.











106 (2018), 234-250. Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.503















(Communicated).

https://doi.org/10.1080/08839514.2016.1185859









0045-7906.




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.





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.










Appendix-C

ITEM-20: DETAILS OF PUBLICATIONS RESULTING FROM THE PROJECT WORK









Factor: 0.50.











106 (2018), 234-250. Publisher: ELSEVIER. Thomson Reuters Impact Factor: 2.503








https://doi.org/10.1080/08839514.2016.1185859












(Communicated).






0045-7906.




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.









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.






final report on the major research project...

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