Polygonal approximation and scale-space analysis

This book covers the most important topics in the area of pattern recognition, object recognition, computer vision, robot vision, medical computing, computational geometry, and bioinformatics systems. Students and researchers will find a comprehensive treatment of polygonal approximation and its real life applications. The book not only explains the theoretical aspects but also presents applications with detailed design parameters. The systematic development of the concept of polygonal approximation of digital curves and its scale-space analysis are useful and attractive to scholars in many fields. Development for different algorithms of polygonal approximation and scale-space analysis and several experimental results with comparative study for measuring the performance of the algorithms are extremely useful for theoretical- and application-oriented works in the above-mentioned areas.

Author Notes

Kumar S. Ray, PhD, is a professor in the Electronics and Communication Science Unit at the Indian Statistical Institute, Kolkata, India. He has written a number of articles published in international journals and has presented at several professional meetings. His current research interests include artificial intelligence, computer vision, commonsense reasoning, soft computing, non-monotonic deductive database systems, and DNA computing.
Bimal Kumar Ray, PhD, is a professor at the School of Information Technology and Engineering. Vellore Institute of Technology, Vellore, India. His research interests include computer graphics, computer vision, and image processing. He has published a number of research papers in peer-reviewed journals.

List of Contributors	p. vii
List of Abbreviations	p. xi
Preface	p. xiii
1 Polygonal Approximation	p. 1
2 A Split and Merge Technique	p. 11
3 A Sequential One-pass Method	p. 21
4 Another Sequential One-pass Method	p. 31
5 A Data-driven Method	p. 45
6 Another Data-driven Method	p. 57
7 A Two-pass Sequential Method	p. 67
8 Polygonal Approximation Using Reverse Engineering on Bresenham's Line Drawing Technique	p. 95
9 Polygonal Approximation as Angle Detection	p. 103
10 Polygonal Approximation as Angle Detection Using Asymmetric Region of Support	p. 113
11 Scale Space Analysis with Application to Corner Detection	p. 125
12 Scale Space Analysis and Corner Detection on Chain Coded Curves	p. 129
13 Scale Space Analysis and Corner Detection Using Iterative Gaussian Smoothing with Constant Window Size	p. 143
14 Corner Detection Using Bessel Function as Smoothing Kernel	p. 175
15 Adaptive Smoothing Using Convolution with Gaussian Kernel	p. 191
16 Application of Polygonal Approximation for Pattern Classification and Object Recognition	p. 199
17 Polygonal Dissimilarity and Scale Preserving Smoothing	p. 203
18 Matching Polygon Fragments	p. 221
19 Polygonal Approximation to Recognize and Locate Partially Occluded Objects Hypothesis Generation and Verification Paradigm	p. 235
20 Object Recognition with Belief Revision: Hypothesis Generation and Belief Revision Paradigm	p. 259
21 Neuro-fuzzy Reasoning for Occluded Object Recognition: A Learning Paradigm through Neuro-fuzzy Concept	p. 311
22 Conclusion	p. 341
Index	p. 369

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Author Notes

Table of Contents