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An Introduction to Object Recognition Selected Algorithms for a Wide Variety of Applications

:	Marco Alexander Treiber
:	An Introduction to Object Recognition Selected Algorithms for a Wide Variety of Applications
:	Springer-Verlag
:	9781849962353
:	Advances in Computer Vision and Pattern Recognition
:	1
:	CHF 48.30
:

:	Anwendungs-Software
:	English

:	202
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum. Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters). Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses. Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly.

	Preface	6
	Acknowledgments	9
	Contents	10
	Abbreviations	14
	1 Introduction	15
	1.1 Overview	15
	1.2 Areas of Application	17
	1.3 Requirements and Constraints	18
	1.4 Categorization of Recognition Methods	21
	References	24
	2 Global Methods	25
	2.1 2D Correlation	25
	2.1.1 Basic Approach	25
	2.1.1.1 Main Idea	25
	2.1.1.2 Example	27
	2.1.1.3 Pseudocode	28
	2.1.1.4 Rating	28
	2.1.2 Variants	29
	2.1.2.1 Variant 1: Preprocessing	29
	2.1.2.2 Variant 2: Subsampling/Image Pyramids	31
	2.1.3 Phase-Only Correlation (POC)	32
	2.1.3.1 Example	33
	2.1.3.2 Pseudocode	34
	2.1.4 Shape-Based Matching	34
	2.1.4.1 Main Idea	34
	2.1.4.2 Example	35
	2.1.4.3 Pseudocode	35
	2.1.4.4 Rating	36
	2.1.5 Comparison	36
	2.2 Global Feature Vectors	38
	2.2.1 Main Idea	38
	2.2.2 Classification	38
	2.2.3 Rating	39
	2.2.4 Moments	39
	2.2.4.1 Main Idea	39
	2.2.4.2 Example	40
	2.2.5 Fourier Descriptors	41
	2.2.5.1 Main Idea	41
	2.2.5.2 Example	41
	2.2.5.3 Modifications	43
	2.2.5.4 Pseudocode	44
	2.3 Principal Component Analysis (PCA)	45
	2.3.1 Main Idea	45
	2.3.2 Pseudocode	48
	2.3.3 Rating	49
	2.3.4 Example	49
	2.3.5 Modifications	51
	References	52
	3 Transformation-Search Based Methods	54
	3.1 Overview	54
	3.2 Transformation Classes	55
	3.3 Generalized Hough Transform	57
	3.3.1 Main Idea	57
	3.3.2 Training Phase	57
	3.3.3 Recognition Phase	58
	3.3.4 Pseudocode	59
	3.3.5 Example	60
	3.3.6 Rating	62
	3.3.7 Modifications	63
	3.4 The Hausdorff Distance	64
	3.4.1 Basic Approach	64
	3.4.1.1 Main Idea	64
	3.4.1.2 Recognition Phase	65
	3.4.1.3 Pseudocode	68
	3.4.1.4 Example	70
	3.4.1.5 Rating	71
	3.4.2 Variants	72
	3.4.2.1 Variant 1: Generalized Hausdorff Distance Generalized Hausdorff distance	72
	3.4.2.2 Variant 2: 3D Hausdorff Distance	72
	3.4.2.3 Variant 3: Chamfer Matching	73
	3.5 Speedup by Rectangular Filters and Integral Images	73
	3.5.1 Main Idea	73
	3.5.2 Filters and Integral Images	74
	3.5.3 Classification	76
	3.5.4 Pseudocode	78
	3.5.5 Example	79
	3.5.6 Rating	80
	References	80
	4 Geometric Correspondence-Based Approaches	82
	4.1 Overview	82
	4.2 Feature Types and Their Detection	83
	4.2.1 Geometric Primitives	84
	4.2.1.1 Polygonal Approximation	84
	4.2.1.2 Approximation with Line Segments and Circular Arcs	84
	4.2.2 Geometric Filters	87
	4.3 Graph-Based Matching	88
	4.3.1 Geometrical Graph Match	88
	4.3.1.1 Main Idea	88
	4.3.1.2 Recognition Phase	89
	4.3.1.3 Pseudocode	91
	4.3.1.4 Example	92
	4.3.1.5 Rating	92
	4.3.2 Interpretation Trees	93
	4.3.2.1 Main Idea	93
	4.3.2.2 Recognition Phase	94
	4.3.2.3 Pseudocode	97
	4.3.2.4 Example	98
	4.3.2.5 Rating	99
	4.4 Geometric Hashing	100
	4.4.1 Main Idea	100
	4.4.2 Speedup by Pre-processing	101
	4.4.3 Recognition Phase	102
	4.4.4 Pseudocode	103
	4.4.5 Rating	104
	4.4.6 Modifications	104
	References	105
	5 Three-Dimensional Object Recognition	107
	5.1 Overview	107
	5.2 The SCERPO System: Perceptual Grouping	109
	5.2.1 Main Idea	109
	5.2.2 Recognition Phase	110
	5.2.3 Example	111
	5.2.4 Pseudocode	111
	5.2.5 Rating	112
	5.3 Relational Indexing	113
	5.3.1 Main Idea	113
	5.3.2 Teaching Phase	114
	5.3.3 Recognition Phase	116
	5.3.4 Pseudocode	117
	5.3.5 Example	118
	5.3.6 Rating	120
	5.4 LEWIS: 3D Recognition of Planar Objects	120
	5.4.1 Main Idea	120
	5.4.2 Invariants	121
	5.4.3 Teaching Phase	123
	5.4.4 Recognition Phase	124
	5.4.5 Pseudocode	125
	5.4.6 Example	126
	5.4.7 Rating	127
	References	128
	6 Flexible Shape Matching	129
	6.1 Overview	129
	6.2 Active Contour Models/Snakes	130
	6.2.1 Standard Snake	130
	6.2.1.1 Main Idea	130
	6.2.1.2 Optimization	131
	6.2.1.3 Example	132
	6.2.1.4 Rating	133
	6.2.2 Gradient Vector Flow Snake	134
	6.2.2.1 Main Idea	134
	6.2.2.2 Pseudocode	135
	6.2.2.3 Example	136
	6.2.2.4 Rating	137
	6.3 The Contracting