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Produktbild: Color in Computer Vision

Color in Computer Vision Fundamentals and Applications

Aus der Reihe Wiley-IS&T Series in Imaging Science and Technology

152,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.09.2012

Verlag

John Wiley & Sons Inc

Seitenzahl

384

Maße (L/B/H)

24/16,1/2,5 cm

Gewicht

745 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-89084-4

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.09.2012

Verlag

John Wiley & Sons Inc

Seitenzahl

384

Maße (L/B/H)

24/16,1/2,5 cm

Gewicht

745 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-89084-4

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Color in Computer Vision
  • Preface xv
     
    1 Introduction 1
    1.1 From Fundamental to Applied 2
    1.2 Part I: Color Fundamentals 3
    1.3 Part II: Photometric Invariance 3
    1.4 Part III: Color Constancy 4
    1.5 Part IV: Color Feature Extraction 5
    1.6 Part V: Applications 7
    1.7 Summary 9
     
    PART I Color Fundamentals 11
     
    2 Color Vision 13
    2.1 Introduction 13
    2.2 Stages of Color Information Processing 14
    2.3 Chromatic Properties of the Visual System 18
    2.4 Summary 24
     
    3 Color Image Formation 26
    3.1 Lambertian Reflection Model 28
    3.2 Dichromatic Reflection Model 29
    3.3 Kubelka-Munk Model 32
    3.4 The Diagonal Model 34
    3.5 Color Spaces 36
    3.6 Summary 44
     
    PART II Photometric Invariance 47
     
    4 Pixel-Based Photometric Invariance 49
    4.1 Normalized Color Spaces 50
    4.2 Opponent Color Spaces 52
    4.3 The HSV Color Space 52
    4.4 Composed Color Spaces 53
    4.5 Noise Stability and Histogram Construction 58
    4.6 Application: Color-Based Object Recognition 64
    4.7 Summary 68
     
    5 Photometric Invariance from Color Ratios 69
    5.1 Illuminant Invariant Color Ratios 71
    5.2 Illuminant Invariant Edge Detection 73
    5.3 Blur-Robust and Color Constant Image Description 74
    5.4 Application: Image Retrieval Based on Color Ratios 77
    5.5 Summary 80
     
    6 Derivative-Based Photometric Invariance 81
    6.1 Full Photometric Invariants 84
    6.2 Quasi-Invariants 101
    6.3 Summary 111
     
    7 Photometric Invariance by Machine Learning 113
    7.1 Learning from Diversified Ensembles 114
    7.2 Temporal Ensemble Learning 119
    7.3 Learning Color Invariants for Region Detection 120
    7.4 Experiments 124
    7.5 Summary 134
     
    PART III Color Constancy 135
     
    8 Illuminant Estimation and Chromatic Adaptation 137
    8.1 Illuminant Estimation 139
    8.2 Chromatic Adaptation 141
     
    9 Color Constancy Using Low-level Features 143
    9.1 General Gray-World 143
    9.2 Gray-Edge 146
    9.3 Physics-Based Methods 150
    9.4 Summary 151
     
    10 Color Constancy Using Gamut-Based Methods 152
    10.1 Gamut Mapping Using Derivative Structures 155
    10.2 Combination of Gamut Mapping Algorithms 157
    10.3 Summary 160
     
    11 Color Constancy Using Machine Learning 161
    11.1 Probabilistic Approaches 161
    11.2 Combination Using Output Statistics 162
    11.3 Combination Using Natural Image Statistics 163
    11.4 Methods Using Semantic Information 167
    11.5 Summary 171
     
    12 Evaluation of Color Constancy Methods 172
    12.1 Data Sets 172
    12.2 Performance Measures 175
    12.3 Experiments 180
    12.4 Summary 185
     
    PART IV Color Feature Extraction 187
     
    13 Color Feature Detection 189
    13.1 The Color Tensor 191
    13.2 Color Saliency 205
    13.3 Conclusions 218
     
    14 Color Feature Description 221
    14.1 Gaussian Derivative-Based Descriptors 225
    14.2 Discriminative Power 229
    14.3 Level of Invariance 235
    14.4 Information Content 236
    14.5 Summary 243
     
    15 Color Image Segmentation 244
    15.1 Color Gabor Filtering 245
    15.2 Invariant Gabor Filters Under Lambertian Reflection 247
    15.3 Color-Based Texture Segmentation 247
    15.4 Material Recognition Using Invariant Anisotropic Filtering 249
    15.5 Color Invariant Codebooks and Material-Specific Adaptation 256
    15.6 Experiments 258
    15.7 Image Segmentation by Delaunay Triangulation 263
    15.8 Summary 268
     
    PART V Applications 269
     
    16 Object and Scene Recognition 271
    16.1 Diagonal Model 272
    16.2 Color SIFT Descriptors 273
    16.3 Object and Scene Recognition 276
    16.4 Results 280
    16.5 Summary 285
     
    17 Color Naming 287
    17.1 Basic Color Terms 288
    17.3 Color Names from Uncalibrated Data 304
    17.4 Experimental Results 313
    17.5 Conclusions 316
     
    18 Segmentation of Multispectral Images 318
    18.1 Reflection and Camera Models 319<