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Applications of Evolutionary Computation in Image Processing and Pattern Recognition

:	Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros
:	Applications of Evolutionary Computation in Image Processing and Pattern Recognition
:	Springer-Verlag
:	9783319264622
:	1
:	CHF 85.50
:

:	Allgemeines, Lexika
:	English

:	274
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an optimization task. The book has been structured so that each chapter can be read independently from the others. It can serve as reference book for students and researchers with basic knowledge in image processing and EC methods.

	Foreword	6
	Preface	8
	Contents	12
	1 Introduction	17
	Abstract	17
	1.1 Definition of an Optimization Problem	17
	1.2 Classical Optimization	18
	1.3 Evolutionary Computation Methods	21
	1.3.1 Structure of an Evolutionary Computation Algorithm	22
	References	24
	2 Image Segmentation Based on Differential Evolution Optimization	25
	Abstract	25
	2.1 Introduction	25
	2.2 Gaussian Approximation	26
	2.3 Differential Evolution Algorithms	27
	2.4 Determination of Thresholding Values	29
	2.5 Experimental Results	30
	2.6 Conclusions	36
	References	37
	3 Motion Estimation Based on Artificial Bee Colony (ABC)	39
	Abstract	39
	3.1 Introduction	39
	3.2 Artificial Bee Colony (ABC) Algorithm	43
	3.2.1 Biological Bee Profile	43
	3.2.2 Description of the ABC Algorithm	43
	3.2.3 Initializing the Population	44
	3.2.4 Send Employed Bees	44
	3.2.5 Select the Food Sources by the Onlooker Bees	45
	3.2.6 Determine the Scout Bees	45
	3.3 Fitness Approximation Method	45
	3.3.1 Updating the Individual Database	46
	3.3.2 Fitness Calculation Strategy	46
	3.3.3 Presented ABC Optimization Method	48
	3.4 Motion Estimation and Block Matching	50
	3.5 BM Algorithm Based on ABC with the Estimation Strategy	51
	3.5.1 Initial Population	52
	3.5.2 The ABC-BM Algorithm	54
	3.6 Experimental Results	55
	3.6.1 ABC-BM Results	55
	3.6.1.1 Distortion Performance	57
	3.6.1.2 Search Efficiency	59
	3.6.2 Results on H.264	59
	3.6.3 Experiments with High Definition Sequences	62
	3.7 Conclusions	64
	References	65
	4 Ellipse Detection on Images Inspired by the Collective Animal Behavior	68
	Abstract	68
	4.1 Introduction	68
	4.2 Collective Animal Behavior Algorithm (CAB)	71
	4.2.1 Description of the CAB Algorithm	71
	4.2.1.1 Initializing the Population	71
	4.2.1.2 Keep the Position of the Best Individuals	72
	4.2.1.3 Move from or to Nearby Neighbors	72
	4.2.1.4 Move Randomly	73
	4.2.1.5 Compete for the Space Within of a Determined Distance (Update the Memory)	73
	4.2.1.6 Computational Procedure	74
	4.3 Ellipse Detection Using CAB	75
	4.3.1 Data Preprocessing	75
	4.3.2 Individual Representation	75
	4.3.3 Objective Function	77
	4.3.4 Implementation of CAB for Ellipse Detection	79
	4.4 The Multiple Ellipse Detection Procedure	80
	4.5 Experimental Results	81
	4.5.1 Ellipse Localization	82
	4.5.1.1 Synthetic Images	82
	4.5.1.2 Natural Images	82
	4.5.2 Shape Discrimination Tests	83
	4.5.3 Ellipse Approximation: Occluded Ellipse and Ellipsoidal Detection	84
	4.5.4 Performance Comparison	84
	4.6 Conclusions	90
	References	91
	5 Template Matching by Using the States of Matter Algorithm	93
	Abstract	93
	5.1 Introduction	93
	5.2 States of Matter	95
	5.3 States of Matter Search (SMS)	97
	5.3.1 Definition of Operators	97
	5.3.1.1 Direction Vector	97
	5.3.1.2 Collision	98
	5.3.1.3 Random Positions	99
	5.3.1.4 Best Element Updating	99
	5.3.2 SMS Algorithm	100
	5.3.2.1 General Procedure	100
	5.3.2.2 The Complete Algorithm	100
	5.3.2.3 Initialization	102
	5.3.2.4 Gas State	103
	5.3.2.5 Liquid State	103
	5.3.2.6 Solid State	104
	5.4 Fitness Approximation Method	104
	5.4.1 Updating Individual Database	105
	5.4.2 Fitness Calculation Strategy	105
	5.4.3 Presented Optimization SMS Method	108
	5.5 Template Matching Process	109
	5.6 TM Algorithm Based on SMS with the Estimation Strategy	110
	5.6.1 The SMS-TM Algorithm	111
	5.7 Experimental Results	113
	5.8 Conclusions	117
	References	118
	6 Estimation of Multiple View Relations Considering Evolutionary Approaches	120
	Abstract	120
	6.1 Introduction	120
	6.2 View Relations from Point Correspondences	123
	6.3 Random Sampling Consensus (RANSAC) Algorithm	126
	6.4 Clonal Selection Algorithm (CSA)	127
	6.4.1 Definitions	127
	6.4.2 CSA Operators	128
	6.4.3 Clonal Proliferation Operator (T_{\rm{P}}^{\rm{C}} )	128
	6.4.4 Affinity Maturation Operator (T_{M}^{\rm{A}} )	129
	6.4.5 Clonal Selection Operator (T_{\rm{S}}^{\rm{C}} )	130
	6.5 Method for Geometric Estimation Using CSA	130
	6.5.1 Computational Procedure	132
	6.6 Experimental Results	135
	6.6.1 Fundamental Matrix Estimation with Synthetic Data	136
	6.6.2 Fundamental Matrix Estimation with Real Images	139
	6.6.3 Homography Estimation with Synthetic Data	142
	6.6.4 Homography Estimation with Real Images	145