Polygonal Approximation and Scale-Space Analysis of Closed Digital Curves

Summary

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
Introduction
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
Introduction
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
Introduction
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
Conclusion

Author(s) Bio

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.