Bio-Inspired Computing for Image and Video Processing

437 Pages·2018·12.412 MB·English

by  Acharjya, D. P., Santhi, V

BIO-INSPIRED
COMPUTING FOR
IMAGE AND VIDEO
PROCESSING

BIO-INSPIRED
COMPUTING FOR
IMAGE AND VIDEO
PROCESSING
D. P. ACHARJYA
V. SANTHI
CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
© 2018 by Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business
No claim to original U.S. Government works
Printed on acid-free paper
Version Date: 20171120
International Standard Book Number-13: 978-1-4987-6592-3 (Hardback)
This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been
made to publish reliable data and information, but the author and publisher cannot assume responsibility for the
validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the
copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to
publish in this form has not been obtained. If any copyright material has not been acknowledged please write and
let us know so we may rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or
utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including
photocopying, microfilming, and recording, or in any information storage or retrieval system, without written
permission from the publishers.
For permission to photocopy or use material electronically from this work, please access www.copyright.com
(http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive,
Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for
a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of
payment has been arranged.
Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only
for identification and explanation without intent to infringe.
Library of Congress Cataloging-in-Publication Data
Names: Acharjya, D. P., 1969- editor. | Santhi, V., 1971- editor.
Title: Bio-inspired computing for image and video processing / [edited by]
D.P. Acharjya and V. Santhi.
Description: Boca Raton : CRC Press, [2017] | Includes bibliographical
references and index.
Identifiers: LCCN 2017022417| ISBN 9781498765923 (hardback : acid-free paper)
| ISBN 9781315153797 (ebook)
Subjects: LCSH: Natural computation. | Image processing--Mathematical models.
| Image analysis--Mathematical models.
Classification: LCC QA76.9.N37 B556 2017 | DDC 006.3/8--dc23
LC record available at https://lccn.loc.gov/2017022417
Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com
and the CRC Press Web site at
http://www.crcpress.com
Dedicated to my beloved mother, Pramodabala Acharjya
D.P. Acharjya
Dedicated to my beloved parents
V. Santhi

Contents
List of Figures ix
List of Tables xvii
Preface xxi
Acknowledgments xxvii
Editors xxix
Contributors xxxi
I Bio-Inspired Computing Models and Algorithms 1
1 Genetic Algorithm and BFOA-Based Iris and Palmprint
Multimodal Biometric Digital Watermarking Models 3
S. Anu H. Nair and P. Aruna
2 Multilevel Thresholding for Image Segmentation Using
Cricket Chirping Algorithm 31
S. Siva Sathya and Jonti Deuri
3 Algorithms for Drawing Graphics Primitives on a
Honeycomb Model-Inspired Grid 59
M. Prabukumar
4 Electrical Impedance Tomography Using Evolutionary
Computing: A Review 93
Wellington Pinheiro dos Santos, Ricardo Emmanuel de Souza, Reiga
Ramalho Ribeiro, Allan Rivalles Souza Feitosa, Valter Augusto de
Freitas Barbosa, Victor Luiz Bezerra Arajo da Silva, David Edson
Ribeiro, and Rafaela Covello de Freitas
II Bio-Inspired Optimization Techniques 129
vii
viii Contents
5 An Optimized False Positive Free Video Watermarking
System in Dual Transform Domain 131
L. Agilandeeswari and K. Ganesan
6 Bone Tissue Segmentation Using Spiral Optimization and
Gaussian Thresholding 161
Hugo Aguirre-Ramos, Juan-Gabriel Avina-Cervantes, and Ivan
Cruz-Aceves
7 Digital Image Segmentation Using Computational
Intelligence Approaches 205
S. Vijayakumar and V. Santhi
8 DigitalColorImageWatermarking UsingDWTSVDCuckoo
Search Optimization 227
S. Ganesh Babu and B. Sarojini Ilango
9 Digital Image Watermarking Scheme in Transform Domain
Using the Particle Swarm Optimization Technique 245
Sarthak Nandi and V. Santhi
III Bio-Inspired Computing Applications to Image
and Video Processing 265
10 Evolutionary Algorithms for the Efficient Design of
Multiplier-Less Image Filter 267
Abhijit Chandra
11 Fusion of Texture and Shape-Based Statistical Features for
MRI Image Retrieval System 297
N. Kumaran and R. Bhavani
12 Singular Value Decomposition–Principal Component
Analysis-Based Object Recognition Approach 323
Chiranji Lal Chowdhary and D.P. Acharjya
13 The KD-ORS Tree: An Efficient Indexing Technique for
Content-Based Image Retrieval 343
N. Puviarasan and R. Bhavani
14 An Efficient Image Compression Algorithm Based on the
Integration of a Histogram Indexed Dictionary and the
Huffman Encoding for Medical Images 369
D.J. Ashpin Pabi, P. Aruna, and N. Puviarasan
Index 395
List of Figures
1.1 Flow diagram of the GA and proposed BFOA watermarking
system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2 Conversion of polar to rectangular form of iris . . . . . . . . 8
1.3 Iris modality extraction. . . . . . . . . . . . . . . . . . . . . 9
1.4 Palmprint modality extraction . . . . . . . . . . . . . . . . . 10
1.5 Sparse representation method . . . . . . . . . . . . . . . . . 18
1.6 Output of different fusion methods, such as average, maxi-
mum, minimum, IHS, and PCA . . . . . . . . . . . . . . . . 19
1.7 Output of different fusion methods, such as Laplacian pyra-
mid, gradient pyramid, DWT, SWT, and sparse representa-
tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.8 Sample output obtained by applying GA watermarking sys-
tem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.9 Sample output obtained by applying BFOA watermarking
system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.10 Performance of BFOA watermarking model vs. other water-
marking models in the literature. . . . . . . . . . . . . . . . 28
2.1 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Cameraman and Zebra) using Kapur’s function 44
2.2 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Sea Fish and Boat Man) using Kapur’s function 44
2.3 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Ostrich and Boat) using Kapur’s function . . 45
2.4 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Tree and Snake) using Kapur’s function . . . 45
2.5 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Cameraman and Zebra) using Otsu’s function 48
2.6 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Sea Star and Boat Man) using Otsu’s function 49
2.7 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Ostrich and Boat) using Otsu’s function . . . 49
2.8 Resultantimagesafter applyingthe CCAto the setofbench-
mark images (Tree and Snake) using Otsu’s function . . . . 50
3.1 Line on hexagonal grid . . . . . . . . . . . . . . . . . . . . . 64
ix