Polygonal Approximation and Scale-Space Analysis of Closed Digital Curves: 1st Edition (Hardback) book cover

Polygonal Approximation and Scale-Space Analysis of Closed Digital Curves

1st Edition

By Kumar S. Ray, Bimal Kumar Ray

Apple Academic Press

388 pages | 153 B/W Illus.

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Hardback: 9781926895338
pub: 2013-02-19
eBook (VitalSource) : 9780429099564
pub: 2013-02-19
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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.

Table of Contents

Part I: Polygonal Approximation


A Split-and-Merge Technique

A Sequential One-Pass Method

Another Sequential One-Pass Method

A Data-Driven Method

Another Data-Driven Method

A Two-Pass Sequential Method

Polygonal Approximation Using Reverse Engineering on Bresenham's Line Drawing Technique

Polygonal Approximation as Angle Detection

Polygonal Approximation as Angle Detection Using Asymmetric Region of Support

Part II: Scale-space analysis


Scale-Space Analysis and Corner Detection on Chain Coded Curves

Scale-Space Analysis and Corner Detection Using Iterative Gaussian Smoothing With Constant Window Size

Corner detection using Bessel function as smoothing kernel

Adaptive smoothing using convolution with Gaussian Kernel

Part III: Application of Polygonal Approximation for Pattern Classification and Object Recognition


Polygonal Dissimilarity and Scale Preserving Smoothing

Matching Polygon Fragments

Polygonal Approximation to Recognize and Locate Partially Occluded Objects: Hypothesis Generation and Verification Paradigm

Object Recognition With Belief Revision: Hypothesis Generation and Belief Revision Paradigm

Neuro-Fuzzy Reasoning for Occluded Object Recognition: A Learning Paradigm Through Neuro-Fuzzy Concept


About the Authors

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 is a professor at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He received his PhD degree in computer science from the Indian Statistical Institute, Kolkata, India. He received hs master’s degree in applied mathematics from Calcutta University and his bachelor’s degree in mathematics from St. Xavier’s College, Kolkata. His research interests include computer graphics, computer vision, and image processing. He has published a number of research papers in peer-reviewed journals.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Arithmetic