LEADER 05566nam 2200745Ia 450 001 9910141438903321 005 20240516114541.0 010 $a1-283-52377-9 010 $a9786613836229 010 $a1-118-35008-1 010 $a1-118-35006-5 010 $a1-118-35007-3 035 $a(CKB)2670000000230367 035 $a(EBL)848541 035 $a(SSID)ssj0000695155 035 $a(PQKBManifestationID)11416230 035 $a(PQKBTitleCode)TC0000695155 035 $a(PQKBWorkID)10675887 035 $a(PQKB)11579275 035 $a(Au-PeEL)EBL848541 035 $a(CaPaEBR)ebr10587610 035 $a(CaONFJC)MIL383622 035 $a(OCoLC)804824387 035 $a(CaSebORM)9781118350065 035 $a(MiAaPQ)EBC848541 035 $a(MiAaPQ)EBC4034479 035 $a(PPN)243040199 035 $a(EXLCZ)992670000000230367 100 $a20120206d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aColor in computer vision $efundamentals and applications /$fTheo Gevers ... [et al.] 205 $a1st ed. 210 $aHoboken, NJ $cWiley$dc2012 215 $a1 online resource (386 p.) 225 1 $aWiley-IS&T Series in Imaging Science and Technology 300 $aDescription based upon print version of record. 311 $a0-470-89084-3 320 $aIncludes bibliographical references and index. 327 $aColor in Computer Vision; Contents; Preface; 1 Introduction; 1.1 From Fundamental to Applied; 1.2 Part I: Color Fundamentals; 1.3 Part II: Photometric Invariance; 1.3.1 Invariance Based on Physical Properties; 1.3.2 Invariance By Machine Learning; 1.4 Part III: Color Constancy; 1.5 Part IV: Color Feature Extraction; 1.5.1 From Luminance to Color; 1.5.2 Features, Descriptors, and Saliency; 1.5.3 Segmentation; 1.6 Part V: Applications; 1.6.1 Retrieval and Visual Exploration; 1.6.2 Color Naming; 1.6.3 Multispectral Applications; 1.7 Summary; PART I Color Fundamentals; 2 Color Vision 327 $a2.1 Introduction2.2 Stages of Color Information Processing; 2.2.1 Eye and Optics; 2.2.2 Retina: Rods and Cones; 2.2.3 Ganglion Cells and Receptive Fields; 2.2.4 LGN and Visual Cortex; 2.3 Chromatic Properties of the Visual System; 2.3.1 Chromatic Adaptation; 2.3.2 Human Color Constancy; 2.3.3 Spatial Interactions; 2.3.4 Chromatic Discrimination and Color Deficiency; 2.4 Summary; 3 Color Image Formation; 3.1 Lambertian Reflection Model; 3.2 Dichromatic Reflection Model; 3.3 Kubelka-Munk Model; 3.4 The Diagonal Model; 3.5 Color Spaces; 3.5.1 XYZ System; 3.5.2 RGB System 327 $a3.5.3 Opponent Color Spaces3.5.4 Perceptually Uniform Color Spaces; 3.5.5 Intuitive Color Spaces; 3.6 Summary; PART II Photometric Invariance; 4 Pixel-Based Photometric Invariance; 4.1 Normalized Color Spaces; 4.2 Opponent Color Spaces; 4.3 The HSV Color Space; 4.4 Composed Color Spaces; 4.4.1 Body Reflectance Invariance; 4.4.2 Body and Surface Reflectance Invariance; 4.5 Noise Stability and Histogram Construction; 4.5.1 Noise Propagation; 4.5.2 Examples of Noise Propagation through Transformed Colors; 4.5.3 Histogram Construction by Variable Kernel Density Estimation 327 $a4.6 Application: Color-Based Object Recognition4.6.1 Dataset and Performance Measure; 4.6.2 Robustness Against Noise: Simulated Data; 4.7 Summary; 5 Photometric Invariance from Color Ratios; 5.1 Illuminant Invariant Color Ratios; 5.2 Illuminant Invariant Edge Detection; 5.3 Blur-Robust and Color Constant Image Description; 5.4 Application: Image Retrieval Based on Color Ratios; 5.4.1 Robustness to Illuminant Color; 5.4.2 Robustness to Gaussian Blur; 5.4.3 Robustness to Real-World Blurring Effects; 5.5 Summary; 6 Derivative-Based Photometric Invariance; 6.1 Full Photometric Invariants 327 $a6.1.1 The Gaussian Color Model6.1.2 The Gaussian Color Model by an RGB Camera; 6.1.3 Derivatives in the Gaussian Color Model; 6.1.4 Differential Invariants for the Lambertian Reflection Model; 6.1.5 Differential Invariants for the Dichromatic Reflection Model; 6.1.6 Summary of Full Color Invariants; 6.1.7 Geometrical Color Invariants in Two Dimensions; 6.2 Quasi-Invariants; 6.2.1 Edges in the Dichromatic Reflection Model; 6.2.2 Photometric Variants and Quasi-Invariants; 6.2.3 Relations of Quasi-Invariants with Full Invariants 327 $a6.2.4 Localization and Discriminative Power of Full and Quasi-Invariants 330 $a While the field of computer vision drives many of today's digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding. Based on the authors' intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, 410 0$aWiley-IS&T series in imaging science and technology. 606 $aComputer vision 606 $aColor vision 606 $aColor photography 615 0$aComputer vision. 615 0$aColor vision. 615 0$aColor photography. 676 $a006.3/7 701 $aGevers$b Theo$0908604 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141438903321 996 $aColor in computer vision$92032117 997 $aUNINA