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.
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vii | |
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xi | |
| Preface |
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xiii | |
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1 Polygonal Approximation |
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1 | (10) |
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2 A Split and Merge Technique |
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11 | (10) |
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3 A Sequential One-pass Method |
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21 | (10) |
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4 Another Sequential One-pass Method |
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31 | (14) |
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45 | (12) |
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6 Another Data-driven Method |
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57 | (10) |
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7 A Two-pass Sequential Method |
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67 | (28) |
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8 Polygonal Approximation Using Reverse Engineering on Bresenham's Line Drawing Technique |
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95 | (8) |
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9 Polygonal Approximation as Angle Detection |
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103 | (10) |
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10 Polygonal Approximation as Angle Detection Using Asymmetric Region of Support |
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113 | (12) |
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11 Scale Space Analysis with Application to Comer Detection |
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125 | (4) |
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12 Scale Space Analysis and Comer Detection on Chain Coded Curves |
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129 | (14) |
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13 Scale Space Analysis and Comer Detection Using Iterative Gaussian Smoothing with Constant Window Size |
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143 | (32) |
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14 Comer Detection Using Bessel Function as Smoothing Kernel |
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175 | (16) |
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15 Adaptive Smoothing Using Convolution with Gaussian Kernel |
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191 | (8) |
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16 Application of Polygonal Approximation for Pattern Classification and Object Recognition |
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199 | (4) |
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17 Polygonal Dissimilarity and Scale Preserving Smoothing |
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203 | (18) |
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18 Matching Polygon Fragments |
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221 | (14) |
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19 Polygonal Approximation to Recognize and Locate Partially Occluded Objects Hypothesis Generation and Verification Paradigm |
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235 | (24) |
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20 Object Recognition with Belief Revision: Hypothesis Generation and Belief Revision Paradigm |
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259 | (52) |
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21 Neuro-fuzzy Reasoning for Occluded Object Recognition: A Learning Paradigm through Neuro-fuzzy Concept |
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311 | (30) |
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341 | (28) |
| Index |
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369 | |
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 masters degree in applied mathematics from Calcutta University and his bachelors degree in mathematics from St. Xaviers 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.
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