Polygonal Approximation and Scale-Space Analysis of Closed Digital Curves

ISBN-13: 9781926895338 / Angielski / Twarda / 2013 / 388 str.

Kumar S. Ray; Bimal Kumar Ray

Opis

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.

Spis treści

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. 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. 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.