Color Constancy

Marc Ebner, Lecturer (Privatdozent), Universitat Wurzburg, Lehrstuhl fur Informatik, Am Hubland, 97074 Wurzburg, Germany Marc Ebner is currently a lecturer at the Department of Computer Science, Programming Languages and Programming Methodology, University of Wurzburg, Germany. He has been at the university since 1999, recently having completed his habilitation dissertation, on which this book is based. He teaches courses on computer graphics and virtual reality and his research interests are in colour constancy, computer vision, self-reproducing programs, neutral networks, and evolutionary algorithms. Previous to this post, he has gained qualifications from Stuttgart University, New York University and Tubingen University. To date, he has authored 8 published journal articles, 29 refereed conference papers.

Series Preface. Preface. 1 Introduction. 1.1 What is Color Constancy? 1.2 Classic Experiments. 1.3 Overview. 2 The Visual System. 2.1 Eye and Retina. 2.2 Visual Cortex. 2.3 On the Function of the Color Opponent Cells. 2.4 Lightness. 2.5 Color Perception Correlates with Integrated Reflectances. 2.6 Involvement of the Visual Cortex in Color Constancy. 3 Theory of Color Image Formation. 3.1 Analog Photography. 3.2 Digital Photography. 3.3 Theory of Radiometry. 3.4 Reflectance Models. 3.5 Illuminants. 3.6 Sensor Response. 3.7 Finite Set of Basis Functions. 4 Color Reproduction. 4.1 Additive and Subtractive Color Generation. 4.2 Color Gamut. 4.3 Computing Primary Intensities. 4.4 CIE XYZ Color Space. 4.5 Gamma Correction. 4.6 Von Kries Coefficients and Sensor Sharpening. 5 Color Spaces. 5.1 RGB Color Space. 5.2 sRGB. 5.3 CIE LuvColor Space. 5.4 CIE LabColor Space. 5.5 CMY Color Space. 5.6 HSI Color Space. 5.7 HSV Color Space. 5.8 Analog and Digital Video Color Spaces. 6 Algorithms for Color Constancy under Uniform Illumination. 6.1 White Patch Retinex. 6.2 The Gray World Assumption. 6.3 Variant of Horna??s Algorithm. 6.4 Gamut-constraint Methods. 6.5 Color in Perspective. 6.6 Color Cluster Rotation. 6.7 Comprehensive Color Normalization. 6.8 Color Constancy Using a Dichromatic Reflection Model. 7 Algorithms for Color Constancy under Nonuniform Illumination. 7.1 The Retinex Theory of Color Vision. 7.2 Computation of Lightness and Color. 7.3 Hardware Implementation of Landa??s Retinex Theory. 7.4 Color Correction on Multiple Scales. 7.5 Homomorphic Filtering. 7.6 Intrinsic Images. 7.7 Reflectance Images from Image Sequences. 7.8 Additional Algorithms. 8 Learning Color Constancy. 8.1 Learning a Linear Filter. 8.2 Learning Color Constancy Using Neural Networks. 8.3 Evolving Color Constancy. 8.4 Analysis of Chromatic Signals. 8.5 Neural Architecture based on Double Opponent Cells. 8.6 Neural Architecture Using Energy Minimization. 9 Shadow Removal and Brightening. 9.1 Shadow Removal Using Intrinsic Images. 9.2 Shadow Brightening. 10 Estimating the Illuminant Locally. 10.1 Local Space Average Color. 10.2 Computing Local Space Average Color on a Grid of Processing Elements. 10.3 Implementation Using a Resistive Grid. 10.4 Experimental Results. 11 Using Local Space Average Color for Color Constancy. 11.1 Scaling Input Values. 11.2 Color Shifts. 11.3 Normalized Color Shifts. 11.4 Adjusting Saturation. 11.5 Combining White Patch Retinex and the Gray World Assumption. 12 Computing Anisotropic Local Space Average Color. 12.1 Nonlinear Change of the Illuminant. 12.2 The Line of Constant Illumination. 12.3 Interpolation Methods. 12.4 Evaluation of Interpolation Methods. 12.5 Curved Line of Constant Illumination. 12.6 Experimental Results. 13 Evaluation of Algorithms. 13.1 Histogram-based Object Recognition. 13.2 Object Recognition under Changing Illumination. 13.3 Evaluation on Object Recognition Tasks. 13.4 Computation of Color Constant Descriptors. 13.5 Comparison to Ground Truth Data. 14 Agreement with Data from Experimental Psychology. 14.1 Perceived Color of Gray Samples When Viewed under Colored Light. 14.2 Theoretical Analysis of Color Constancy Algorithms. 14.3 Theoretical Analysis of Algorithms Based on Local Space Average Color. 14.4 Performance of Algorithms on Simulated Stimuli. 14.5 Detailed Analysis of Color Shifts. 14.6 Theoretical Models for Color Conversion. 14.7 Human Color Constancy. 15 Conclusion. Appendix A Dirac Delta Function. Appendix B Units of Radiometry and Photometry. Appendix C Sample Output from Algorithms. Appendix D Image Sets. Appendix E Program Code. Appendix F Parameter Settings. Bibliography. List of Symbols. Index. Permissions.