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Artificial neural networks for image understanding

Artificial neural networks for image understanding

자료유형
단행본
개인저자
Kulkarni, Arun D. , 1947-.
서명 / 저자사항
Artificial neural networks for image understanding / Arun D. Kulkarni.
발행사항
New York :   Van Nostrand Reinhold ,   c1994.  
형태사항
xi, 210 p. : ill. ; 25 cm.
총서사항
VNR computer library
ISBN
0442009216 9780442009212
서지주기
Includes bibliographical references and index.
일반주제명
Neural networks (Computer science) Image processing.
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008 930517s1994 nyua b 001 0 eng
010 ▼a 93004892
020 ▼a 0442009216
020 ▼a 9780442009212
035 ▼a (KERIS)REF000014711453
040 ▼a DLC ▼c DLC ▼d DLC ▼d 211009
050 0 0 ▼a QA76.87 ▼b .K84 1994
082 0 0 ▼a 006.3/7 ▼2 22
090 ▼a 006.37 ▼b K96a
100 1 ▼a Kulkarni, Arun D. , ▼d 1947-.
245 1 0 ▼a Artificial neural networks for image understanding / ▼c Arun D. Kulkarni.
260 ▼a New York : ▼b Van Nostrand Reinhold , ▼c c1994.
300 ▼a xi, 210 p. : ▼b ill. ; ▼c 25 cm.
440 0 ▼a VNR computer library
504 ▼a Includes bibliographical references and index.
650 0 ▼a Neural networks (Computer science)
650 0 ▼a Image processing.
945 ▼a KINS

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.37 K96a 등록번호 121162883 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차


CONTENTS
Preface = Ⅸ
Acknowledgments = XI
1 Introduction = 1
 1.1 Artificial Neural Network Models = 1
 1.2 Image Understanding = 7
 References = 11
2 Preprocessing = 13
 2.1 Introduction = 13
 2.2 Gray Scale Manipulation Techniques = 14
 2.3 Edge Enhancement Techniques = 17
 2.4 ANN Models for Brightness Perception and Boundary Detection = 21
 2.5 Noise Remval Techniques = 28
 2.6 Image Restoration = 31
 2.7 Interpolation = 41
 2.8 Summary = 46
 References = 46
3 Feature Extrantion = 49
 3.1 Introduction = 49
 3.2 Feature Extraction Using Moment Invariants = 50
 3.3 Feature Extraction Using Orthogonal Transforms = 53
 3.4 Fourier Transform Domain Feature Extraction = 55
 3.5 ANN Model for FT Domain Feature Extrantion = 58
 3.6 Artificial Neural Network Model Using WHT Domain Feature Extraction = 67
 3.7 Invariant Feature Extraction Using ADALINE = 70
 3.8 Summary = 73
 References = 73
4 Texture Analysis = 75
 4.1 Introduction = 75
 4.2 Statical Methods = 76
 4.3 Spectral Approaches = 78
 4.4 Artificial Neural Network Models for Texture Analysis = 83
 4.5 Summary = 90
 References = 91
5 Supervised Classifiers = 93
 5.1 Introduction = 93
 5.2 Conventional Classifiers = 94
 5.3 ANN Models as Classofoers = 98
 5.4 Summary = 107
 References = 107
6 Unsupervised Classifiers = 109
 6.1 Introduction = 109
 6.2 Conventional Clustering Techniques = 110
 6.3 Self-Organizing Netwurks = 113
 6.4 Summary = 125
 References = 125
7 Associative Memories = 128
 7.1 Introduction = 128
 7.2 Bidirectional Associative Memory = 129
 7.3 Optimal Associative Memory = 133
 7.4 Selective Reflex Memory = 135
 7.5 Hopfield Network as an Autoassociative Memory = 136
 7.6 Bidirectional Associative Memories with Multiple Input / Output Patterns = 138
 7.7 Temporal Associative Memories = 139
 7.8 Counterpropagation Networks as Associative Memory = 140
 7.9 Image-Processing Applications = 143
 7.10 Summary = 146
 References = 147
8 Three-Dimensional Structures and Motion = 149
 8.1 Introduction = 149
 8.2 Optical Flow = 150
 8.3 Computation of Optical Flow Using Neural Networks = 151
 8.4 Estimation of 3-D Motion Paramerters = 154
 8.5 Neural Networks for Motion Estomation = 157
 8.6 Stereo Vision = 159
 8.7 Stereopsis with Neural Networks = 162
 8.8 Summary = 165
 References = 165
9 Neurocomputing = 168
 9.1 Introduction = 168
 9.2 Digital Neural Network Processors = 169
 9.3 Analog Netrocomputers = 175
 9.4 Summary = 178
 References = 179
10 Applications = 180
 10.1 Introduction = 180
 10.2 Remote Sensing = 180
 10.3 Medical Image Processing = 183
 10.4 Fingerprint Processing = 184
 10.5 Character Recognition = 186
 10.6 Characterization of Faces = 190
 10.7 Data Conpression = 191
 10.8 Knowledge-Based Pattern Recognition = 194
 10.9 Extraction of Weak Targets in a High-Clutter Environment = 201
 10.10 Summary = 202
 References = 203
Index = 205


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