Digital Color Image Processing

ISBN-13: 9780470147085 / Angielski / Twarda / 2008 / 400 str.

Andreas Koschan; Mongi A. Abidi

Opis

An introduction to color in three-dimensional image processing and the emerging area of multi-spectral image processing The importance of color information in digital image processing is greater than ever. However, the transition from scalar to vector-valued image functions has not yet been generally covered in most textbooks. Now, Digital Color Image Processing fills this pressing need with a detailed introduction to this important topic. In four comprehensive sections, this book covers:

• The fundamentals and requirements for color image processing from a vector-valued viewpoint
• Techniques for preprocessing color images
• Three-dimensional scene analysis using color information, as well as the emerging area of multi-spectral imaging
• Applications of color image processing, presented via the examination of two case studies

In addition to introducing readers to important new technologies in the field, Digital Color Image Processing also contains novel topics such as: techniques for improving three-dimensional reconstruction, three-dimensional computer vision, and emerging areas of safety and security applications in luggage inspection and video surveillance of high-security facilities. Complete with full-color illustrations and two applications chapters, Digital Color Image Processing is the only book that covers the breadth of the subject under one convenient cover. It is written at a level that is accessible for first- and second-year graduate students in electrical and computer engineering and computer science courses, and that is also appropriate for researchers who wish to extend their knowledge in the area of color image processing.

Recenzje

This book is recommended for first and second–year graduate students in electrical and computing engineer and computer science courses, as well as for researchers with basic knowledge in image processing who wish to extend their knowledge in the area of color image processing. ( Blogcritics.org, April 30, 2008)

Spis treści

1. Introduction. 1.1 Goal and Content of This Book. 1.2 Terminology in Color Image Processing. 1.2.1 What Is A Digital Color Image? 1.2.2 Derivative of a Color Image. 1.2.3 Color Edges. 1.2.4 Color Constancy. 1.2.5 Contrast of a Color Image. 1.2.6 Noise in Color Images. 1.2.7 Luminance, Illuminance, and Brightness. 1.3 Color Image Analysis in Practical Use. 1.4 References to Further Reading. 1.5 References. 2. Eye and Color. 2.1 Physiology of Color Vision. 2.2 Receptoral Color Information. 2.3 Postreceptoral Color Information. 2.4 Cortical Color Information. 2.5 References. 3. Color Spaces and Color Distances. 3.1 Standard Color System. 3.1.1 CIE Color Matching Functions. 3.1.2 Standard Color Values. 3.1.3 Chromaticity Diagrams. 3.1.4 Macadam Ellipses 48. 3.2 Physics And Technics–Based Color Spaces. 3.2.1 RRGBb Color Spaces. 3.2.2 CMY(K) Color Space. 3.2.3 YIQ Color Space. 3.2.4 YUV Color Space. 3.2.5 YC B C RColor Space. 3.2.6 Kodak PhotoCD YC 1 C 2 Color Space. 3.2.7 I 1 I 2 I 3 Color Space. 3.3 Uniform Color Spaces. 3.3.1 CIELAB Color Space. 3.3.2 CIELUV Color Space. 3.4 Perception–Based Color Spaces. 3.4.1 HSI Color Space. 3.4.2 HSV Color Space. 3.4.3 Opponent Color Spaces. 3.5 Color Difference Formulae. 3.5.1 Color Difference Formulae in the RGB Color Space. 3.5.2 Color Difference Formulae in the HSI Color Space. 3.5.3 Color Difference Formulae in the CIELAB and CIELUV Color Spaces. 3.6 Color Ordering Systems. 3.6.1 Munsell Color System. 3.6.2 Macbeth–Colorchecker. 3.6.3 DIN Color Map. 3.7 References. 4. Color Image Formation. 4.1 Technical Design of Electronic Color Cameras. 4.1.1 Image Sensors. 4.1.2 Multispectral Imaging Using Black And White Cameras with Color Filters. 4.1.3 One–Chip CCD Color Camera. 4.1.4 Three–Chip CCD Color Cameras. 4.1.5 Digital Cameras. 4.2 Standard Color Filters and Standard Illuminants. 4.2.1 Standard Color Filters. 4.2.2 Standard Illuminants. 4.3 Photometric Sensor Model. 4.3.1 Attenuation, Clipping, and Blooming. 4.3.2 Chromatic Aberration 93. 4.3.3 Correction of the Chromatic Aberration. 4.4 Photometric and Colorimetric Calibration. 4.4.1 Nonlinearities of Camera Signals. 4.4.2 Measurement of Camera Linearity. 4.4.3 White Balance and Black Level Determination. 4.4.4 Transformation into the Standard Color System XYZ. 4.5 References. 5. Color Image Enhancement. 5.1 False Colors and Pseudo Colors. 5.2 Enhancement of Real Color Images. 5.3 Noise Removal in Color Images. 5.3.1 Box–Filter. 5.3.2 Median Filter. 5.3.3 Morphological Filter. 5.3.4 Filtering In the Frequency Domain. 5.4 Contrast Enhancement in Color Images. 5.4.1 Treatment of Color Saturation and Lightness. 5.4.2 Changing the Hue. 5.5 References. 6. Edge Detection in Color Images. 6.1 Vector–Valued Techniques. 6.1.1 Color Variants of the Canny Operator. 6.1.2 Cumani Operator. 6.1.3 Operators Based On Vector Order Statistics. 6.2 Results of Color Edge Operators. 6.3 Classification of Edges. 6.3.1 Physics–Based Classification. 6.3.2 Classification Applying Photometric Invariant Gradients. 6.4 Color Harris Operator. 6.4 References. 7. Color Image Segmentation. 7.1 Pixel–Based Segmentation. 7.1.1 Histogram Techniques. 7.1.2 Cluster Analysis in the Color Space. 7.2 Area–Based Segmentation. 7.2.1 Region Growing Technique. 7.2.2 Split and Merge. 7.3 Edge–Based Segmentation. 7.3.1 Local Techniques. 7.3.2 Segmentation by Watershed Transformation. 7.4 Physics–Based Segmentation. 7.4.1 Dichromatic Reflection Model. 7.4.2 Classification Techniques. 7.5 Comparison of Segmentation Processes. 7.6 References. 8. Highlights, Interreflections, and Color Constancy. 8.1 Highlight Analysis in Color Images. 8.1.1 Klinker–Shafer–Kanade Technique. 8.1.2 Tong–Funt Technique. 8.1.3 Gershon–Jepson–Tsotsos Technique. 8.1.4 SchlNs–Teschner Technique. 8.1.5 Spectral Differencing Using Several Images. 8.1.6 Photometric Multi–Image Technique. 8.1.7 Polarization Technique. 8.2 Interreflection Analysis in Color Images. 8.2.1 One–Bounce Model for Interreflections. 8.2.2 Minimization of Interreflections in Real Color Images. 8.3 Color Constancy. 8.3.1 A Mathematical Formulation of the Color Constancy Problem. 8.3.2 Techniques for Color Constancy. 8.4 References. 9. Static Stereo Analysis in Color Images. 9.1 Geometry of a Stereo Image Acquisition System. 9.2 Area–Based Correspondence Analysis. 9.2.1 Dense Disparity Maps by Chromatic Block–Matching. 9.2.3 Hierarchic Block–Matching In a Color Image Pyramid. 9.2.4 Stereo Analysis with Color Pattern Projection. 9.3 Feature–Based Correspondence Analysis. 9.3.1 Edge–Based Correspondence Analysis. 9.3.2 General Ideas. 9.4 References. 10. Dynamic and Photometric Stereo Analyses in Color Images. 10.1 Optical Flow. 10.1.1 Solutions. 10.1.2 Horn–Schunck Constraint for Color Image Sequences. 10.2 Photometric Stereo Analysis. 10.2.1 Photometric Stereo Analysis for Non–Static Scenes. 10.2.2 Photometric Stereo Analysis for Non–Lambertian Surfaces. 10.3 References. 11. Color–Based Tracking with PTZ Cameras. 11.1 The Background Problem. 11.2 Methods for Tracking. 11.2.1 Active Shape Models. 11.2.2 Automatic Target Acquisition and Handover from Fixed To PTZ Camera. 11.2.3 Color and Predicted Direction and Speed of Motion. 11.3 Technical Aspects of Tracking. 11.3.1 Feature Extraction for Zooming and Tracking. 11.3.2 Color Extraction from a Moving Target. 11.4 Color Active Shape Models. 11.4.1 Landmark Points. 11.4.2 Principal Component Analysis. 11.4.3 Model Fitting. 11.4.4 Modeling a Local Structure. 11.4.5 Hierarchical Approach for Multi–Resolution ASM. 11.4.6 Extending ASMS to Color Image Sequences. 11.4.7 Selecting the Number of Landmark Points. 11.4.8 Partial Occlusions. 11.4.9 Summary. 11.5 References. 12. Multispectral Imaging for Biometrics. 12.1 What Is A Multispectral Image?. 12.2 Multispectral Image Acquisition. 12.3 Fusion Of Visible And Infrared Images For Face Recognition. 12.3.1 Registration Of Visible And Thermal Face Images. 12.3.2 Empirical Mode Decomposition. 12.3.3 Image Fusion Using EMD. 12.3.4 Experimental Results. 12.4 Multispectral Image Fusion in the Visible Spectrum for Face Recognition. 12.4.1 Physics–Based Weighted Fusion. 12.4.2 Illumination Adjustment Via Data Fusion. 12.4.3 Wavelet Fusion. 12.4.4 Cmc Measure. 12.4.5 Multispectral, Multimodal and Multi–Illuminant IrRIS–M 3 12.4.6 Experimental Results. 12.5 References. 13. Pseudo–Coloring in Single Energy X–Ray Images. 13.1 Problem Statement. 13.2 Aspects of The Human Perception Of Color. 13.2.1 Physiological Processing Of Color. 12.2.2 Psychological Processing Of Color. 12.2.3 General Recommendations for Optimum Color Assignment. 13.2.4 Physiologically–Based Guidelines. 13.2.5 Psychologically–Based Guidelines. 13.3 Theoretical Aspects of Pseudo–Coloring. 13.4 RGB–Based Color Maps. 13.4.1 Perceptually–Based Color Maps. Linear Mapping. Non–Linear Mapping. 13.4.2 Mathematical Formulations. Algebraic Transforms. Sine/Cosine Transform. Rainbow Transform. 13.5 HSI Based Color Maps. 13.5.1 Mapping of Raw Grayscale Data. Histogram–Based Color–Mapping. Function–Based Mapping. 13.5.2 Color Applied To Preprocessed Gray Scale Data. Constant Saturation. Variable Saturation (VS). 13.6 Experimental Results. 13.6.1 Color–Coded Images Generated By RGB–Based Transforms. 13.6.2 Color–Coded Images Generated By HSI–Based Transforms. 13.7 Performance Evaluation. 13.7.1 Preliminary On–Line Survey. 13.7.2 Formal Airport Evaluation. 13.8 Conclusion. 13.9 References.

O autorze

Andreas Koschan, PhD, is a Research Associate Professor with the Department of Electrical Engineering and Computer Science, University of Tennessee. His research interests include three–dimensional computer vision and, in particular, shape recovery using binocular stereo vision and laser range–finding techniques. He is also interested in the utilization of color information in digital image processing. He is the coauthor of three other books.

Mongi Abidi, PhD, is a Professor and Associate Head in the Department of Electrical Engineering and Computer Science, University of Tennessee. Dr. Abidi directs activities in the Imaging, Robotics, and Intelligent Systems Laboratory, and conducts research in the field of three–dimensional imaging, specifically in the areas of scene building, scene description, and data visualization.