A Beginner’s Guide to Image Shape Feature Extraction Techniques: 1st Edition (Hardback) book cover

A Beginner’s Guide to Image Shape Feature Extraction Techniques

1st Edition

By Jyotismita Chaki, Nilanjan Dey

CRC Press

152 pages | 91 B/W Illus.

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Hardback: 9780367254391
pub: 2019-08-29
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This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics and information technology.

Table of Contents

Chapter 1: Introduction to Shape Feature

1.1 Introduction to Shape Feature

1.2 Importance of Shape Features

1.3 Properties of Efficient Shape Features

1.4 Types of Shape features

1.5 Summary

Chapter 2: One Dimensional Function Shape Features

2.1 Complex Coordinate

2.2 Centroid Distance Function

2.3 Tangent Angle

2.4 Contour Curvature

2.5 Area Function

2.6 Triangle Area Representation

2.7 Cord Length Function

2.8 Summary

Chapter 3: Geometric Shape Features

3.1 Center of Gravity

3.2 Axis of Minimum Inertia

3.3 Average Bending Energy

3.4 Eccentricity

3.5 Circularity ratio

3.6 Ellipticity

3.7 Rectangularity

3.8 Convexity

3.9 Solidity

3.10 Euler Number

3.11 Profiles

3.12 Hole Area Ratio

3.13 Summary

Chapter 4: Polygonal Approximation Shape Features

4.1 Merging Method

4.2 Splitting Method

4.3 Minimum Perimeter Polygon

4.4 Dominant Point Detection

4.5 K-means Method

4.6 Genetic Algorithm

4.7 Ant Colony Optimization Method

4.8 Tabu Search Method

4.9 Summary

Chapter 5: Spatial Interrelation Shape Features

5.1 Adaptive Grid Resolution

5.2 Bounding Box

5.3 Convex-Hull

5.4 Chain Code

5.5 Smooth Curve Decomposition

5.6 Beam Angle Statistics

5.7 Shape Matrix

5.8 Shape Context

5.9 Chord Distribution

5.10 Shock Graphs

5.11 Summary


Chapter 6: Moments Shape Feature

6.1 Contour Moment

6.2 Geometric Invariant Moment

6.3 Zernike Moment

6.4 Radial Chebyshev Moment

6.5 Legendre Moment

6.6 Homocentric Polar-Radius Moment

6.7 Orthogonal Fourier-Mellin Moment

6.8 Pseudo-Zernike Moment

6.9 Summary

Chapter 7: Scale Space Shape Features

7.1 Curvature Scale Space

7.2 Morphological Scale Space

7.3 Intersection Points Map

7.4 Summary

Chapter 8: Shape Transform Domain Shape Features

8.1 Fourier Descriptors

8.2 Wavelet Transforms

8.3 Angular Radial Transformation

8.4 Shape Signature Harmonic Embedding

8.5 -Transform

8.6 Shapelets Descriptor

8.7 Summary

Chapter 9: Applications of Shape Features

9.1 Digit Recognition

9.2 Character Recognition

9.3 Fruit Recognition

9.4 Leaf Recognition

9.5 Hand Gesture Recognition

9.6 Summary

About the Authors

Dr. Jyotismita Chaki is an Asst. Professor in the Department of Information Technology ang Engineering in Vellore Institute of Technology, Vellore, India. She has done her PhD (Engg) in digital image processing from Jadavpur University, Kolkata, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Data mining, Machine learning. She has published one book and 22 international conferences and journal papers. She has also served as a Program Committee member of 2nd International Conference on Advanced Computing and Intelligent Engineering 2017 (ICACIE-2017), 4TH International Conference on Image Information Processing (ICIIP-2017).

Dr. Nilanjan Day was born in Kolkata, India, in 1984. He received his B.Tech. degree in Information Technology from West Bengal University of Technology in 2005,M.Tech. in InformationTechnology in 2011 fromthe same University and Ph.D. in digital image processing in 2015 from Jadavpur University, India. In 2011, he was appointed as an Asst. Professor in the Department of Information Technology at JIS College of Engineering, Kalyani, India followed by Bengal College of Engineering College, Durgapur, India in 2014. He is now employed as an Asst. Professor in Department of Information Technology, Techno India College of Technology, India. His research topic is signal processing, machine learning and information security. Dr. Dey is an Associate Editor of IEEE ACCESS and is currently the Editor in-Chief of the International Journal of Ambient Computing and Intelligence, and Series Editor of Springer Tracts in Nature-Inspired Computing (STNIC).

About the Series

Intelligent Signal Processing and Data Analysis

In the current era of huge amount of data types and measurement in all sectors and applications, the same requires automated capturing, data analysis and evaluation methods. Consequently, sophisticated intelligent approaches become essential as flexible and powerful tools based on different signal processing algorithms for multiple applications. Intelligent signal processing (ISP) methods are progressively swapping the conventional analog signal processing techniques in several domains, such as speech analysis and processing, biomedical signal analysis radar and sonar signal processing, and processing, telecommunications, and geophysical signal processing. The main focus is this book series is to find out the new trends and techniques in the intelligent signal processing and data analysis leading to scientific breakthroughs in applied applications. Artificial fuzzy logic, deep learning, optimization algorithms, and neural networks are presented for signal processing applications. The series main emphasis is to offer both extensiveness diversity of selected intelligent signal processing and depth in the selected state-of-the-art data analysis techniques for solving real world problems.

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Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Image Processing