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E-raamat: Beginners Guide to Image Shape Feature Extraction Techniques [Taylor & Francis e-raamat]

(Vellore Inst. of Technology, Vellore, India),
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Beginners Guide to Image Shape Feature Extraction TechniquesSuurem pilt
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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.

Preface ix
Authors xiii
1 Introduction to Shape Feature 1(14)
1.1 Introduction
1(5)
1.1.1 4-Neighborhood
2(1)
1.1.2 d-Neighborhood
3(1)
1.1.3 8-Neighborhood
3(1)
1.1.4 Connectivity
4(1)
1.1.5 Connected Components
5(1)
1.2 Importance of Shape Features
6(2)
1.3 Properties of Efficient Shape Features
8(1)
1.4 Types of Shape Features
9(3)
1.4.1 Contour-Based Shape Representation and Description Techniques
10(1)
1.4.1.1 Global Methods
10(1)
1.4.1.2 Structural Methods
10(1)
1.4.1.3 Limitations of the Structural Approach
10(1)
1.4.2 Region-Based Shape Representation and Description Techniques
11(1)
1.5 Summary
12(1)
References
12(3)
2 One-Dimensional Function Shape Features 15(10)
2.1 Complex Coordinate (ComC)
15(1)
2.2 Centroid Distance Function (CDF)
16(1)
2.3 Tangent Angle (TA)
17(1)
2.4 Contour Curvature (CC)
18(2)
2.5 Area Function (AF)
20(1)
2.6 Triangle Area Representation (TAR)
21(1)
2.7 Chord Length Function (CLF)
22(1)
2.8 Summary
23(1)
References
23(2)
3 Geometric Shape Features 25(20)
3.1 Center of Gravity (CoG)
25(2)
3.2 Axis of Minimum Inertia (AMI)
27(1)
3.3 Average Bending Energy (ABE)
28(1)
3.4 Eccentricity
29(2)
3.4.1 Principal Axes Method
29(1)
3.4.2 Minimum Bounding Rectangle (MBR)
30(1)
3.5 Circularity Ratio (CR)
31(2)
3.6 Ellipticity
33(2)
3.6.1 Ellipse Variance (EV)
33(1)
3.6.2 Ellipticity Based on Moment Invariants
34(1)
3.7 Rectangularity
35(2)
3.7.1 Smallest Bounding Rectangle (SBR)
35(1)
3.7.2 Rectangular Discrepancy Method (RDM)
35(1)
3.7.3 Robust Smallest Bounding Rectangle (RSBR)
36(1)
3.8 Convexity
37(1)
3.9 Solidity
38(1)
3.10 Euler Number (EN)
39(1)
3.11 Profiles
39(1)
3.12 Hole Area Ratio (HAR)
40(1)
3.13 Summary
41(1)
References
41(4)
4 Polygonal Approximation Shape Features 45(20)
4.1 Merging Method (MM)
46(2)
4.1.1 Distance Threshold Method (DTM)
46(1)
4.1.2 Tunnelling Method (TM)
47(1)
4.1.3 Polygon Evolution by Vertex Deletion (PEVD)
47(1)
4.2 Splitting Method (SM)
48(2)
4.3 Minimum Perimeter Polygon (MPP)
50(2)
4.3.1 Data Preparation for MPP
51(1)
4.3.2 MPP Algorithm
51(1)
4.4 Dominant Point (DP) Detection
52(1)
4.5 K-means Method
53(1)
4.6 Genetic Algorithm (GA)
54(3)
4.6.1 Encoding
55(1)
4.6.2 Fitness
56(1)
4.6.3 Genetic Operators or Control Parameters
56(1)
4.7 Ant Colony Optimization (ACO) Method
57(2)
4.7.1 Initialization
57(1)
4.7.2 Node Transition Rule
57(1)
4.7.3 Pheromone Updating Rule
58(1)
4.7.4 Stopping Criterion
58(1)
4.8 Tabu Search (TS)
59(2)
4.8.1 Initialization
60(1)
4.8.2 Definition of Moves
60(1)
4.8.3 Aspiration Criteria (AC)
61(1)
4.9 Summary
61(1)
References
62(3)
5 Spatial Interrelation Shape Features 65(16)
5.1 Adaptive Grid Resolution (AGR)
65(1)
5.2 Bounding Box (BB)
66(1)
5.3 Convex Hull (CH)
67(1)
5.4 Chain Code (CC)
68(2)
5.4.1 Basic
68(1)
5.4.2 Differential
69(1)
5.4.3 Re-sampling
69(1)
5.4.4 Vertex
69(1)
5.4.5 Chain Code Histogram (CCH)
69(1)
5.5 Smooth Curve Decomposition (SCD)
70(1)
5.6 Beam Angle Statistics (BAS)
71(1)
5.7 Shape Matrix (SM)
72(2)
5.7.1 Square Model
72(1)
5.7.2 Polar Model
73(1)
5.8 Shape Context (SC)
74(1)
5.9 Chord Distribution (CD)
75(1)
5.10 Shock Graphs (SG)
76(1)
5.11 Summary
77(1)
References
78(3)
6 Moment Shape Feature 81(14)
6.1 Contour Moment (CM)
81(1)
6.2 Geometric Invariant Moment (GIM)
82(1)
6.3 Zernike Moment (ZM)
83(2)
6.4 Radial Chebyshev Moment (RCM)
85(1)
6.5 Legendre Moment (LM)
86(2)
6.6 Homocentric Polar-Radius Moment (HPRM)
88(1)
6.7 Orthogonal Fourier-Mellin Moment (OFMM)
89(2)
6.8 Pseudo-Zernike Moment (PZM)
91(1)
6.9 Summary
92(1)
References
93(2)
7 Scale-Space Shape Features 95(12)
7.1 Curvature Scale Space (CSS)
95(5)
7.1.1 Extreme Curvature Scale Space (ECSS)
98(1)
7.1.2 Direct Curvature Scale Space (DCSS)
98(1)
7.1.3 Affine Resilient Curvature Scale Space (ARCSS)
99(1)
7.2 Morphological Scale Space (MSS)
100(3)
7.3 Intersection Points Map (IPM)
103(1)
7.4 Summary
104(1)
References
104(3)
8 Shape Transform Domain Shape Feature 107(14)
8.1 Fourier Descriptors
107(3)
8.1.1 One-Dimensional Fourier Descriptors
107(1)
8.1.2 Region-Based Fourier Descriptor
108(2)
8.2 Wavelet Transform
110(4)
8.3 Angular Radial Transformation (ART)
114(1)
8.4 Shape Signature Harmonic Embedding
115(1)
8.5 9i Transform
116(2)
8.6 Shapelet Descriptor (SD)
118(1)
8.7 Summary
119(1)
References
120(1)
9 Applications of Shape Features 121(10)
9.1 Digit Recognition
121(1)
9.2 Character Recognition
122(1)
9.3 Fruit Recognition
123(2)
9.4 Leaf Recognition
125(2)
9.5 Hand Gesture Recognition
127(2)
9.6 Summary
129(1)
References
129(2)
Index 131
Dr. Jyotismita Chaki is an Asst. Professor in the Department of Information Technology and 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).
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