Visual Computing

1. Introduction.- 1.1. The Concept of Visual Computing.- 1.2. Organization of the Book.- 2. Psyschophysical Basics.- 2.1. Anatomy of the Human Visual System.- 2.1.1. Overview.- 2.1.2. Biological Neurons.- 2.1.3. Receptive Fields.- 2.1.4. The Human Retina.- 2.1.5. Organization of the Visual Cortex.- 2.2. Physics of the Human Eye.- 2.2.1. Image Projection and the Field of Vision.- 2.2.2. Accommodation.- 2.2.3. Image Quality and Diffraction Effects.- 2.3. Measuring Light.- 2.3.1. Spectral Sensitivity.- 2.3.2. Basic Measurements.- 2.3.3. Examples.- 2.4. Rendering Physically Based Light Sources.- 2.4.1. A Rendering Pipeline.- 2.4.2. Modeling Light Sources.- 2.4.3. Direct Illumination.- 2.4.4. Spectral Radiosity.- 2.4.5. Spectral Ray Tracing.- 2.4.6. Examples.- 3. Sensitivity to Light and Color.- 3.1. Visual Perception of Light and Shape.- 3.1.1. Adaptation.- 3.1.2. Spatial Sensitivity.- 3.1.3. Temporal Sensitivity.- 3.1.4. Binocular Vision.- 3.1.5. Visual Clustering, Grouping and Gestalt.- 3.2. Color Vision.- 3.2.1. Physiological Basics.- 3.2.2. Measuring Color.- 3.3. Imaging Transforms.- 4. Visualization and Visibility Analysis.- 4.1. Introduction.- 4.2. Visibility Analysis Using Graphics and Imaging.- 4.2.1. Introductory Remarks.- 4.2.2. Factors Influencing the Visibility.- 4.2.3. Mathematical Description of the Visibility.- 4.2.4. Image Generation and Image Analysis.- 4.3. Interactive Visualization and Simulation.- 4.3.1. Introductory Remarks.- 4.3.2. Modeling of Wind and Air Pollution.- 4.3.3. Shape and Color for Visualization.- 4.3.4. Examples.- 4.3.5. The Need for Advanced Imaging Methods.- 5. Computational Vision.- 5.1. Introduction: The Marr Paradigm.- 5.2. Early Visual Processing.- 5.2.1. Basics.- 5.2.2. Modeling Retinal Image Processing.- 5.2.3. Modeling Cortical Image Processing.- 5.3. Advanced Visibility Analysis for Advertising.- 5.3.1. The Psychology of Advertising.- 5.3.2. Analyzing Retinal and Cortical Images.- 5.3.3. Modeling via Ray Casting.- 5.3.4. Examples.- 5.4. Wavelets for Graphics and Imaging.- 5.4.1. An Introduction to Wavelet Bases.- 5.4.2. General Description of the CWT.- 5.4.3. Nonorthogonal Wavelets.- 5.4.4. Wavelets for Volume Rendering.- 5.4.5. Wavelets for Texture Analysis.- 5.5. Shape from Stereo.- 5.5.1. Introductory Remarks.- 5.5.2. Automatic Stereo Matching.- 5.5.3. Formulation of a Matching Algorithm.- 5.5.4. Examples.- 5.6. Active Light Approaches: Laser Scanners.- 6. Image Analysis and Neural Networks.- 6.1. Introductory Remarks.- 6.2. Mathematical Foundations.- 6.2.1. Cluster Analysis and VectorQuantization.- 6.2.2. N-Tree Clustering.- 6.2.3. Dimensionality Reduction and Ordering.- 6.2.4. Principal Components and Subspaces.- 6.2.5. Image Coding Using the KL-Transform.- 6.2.6. Supervised Classification Methods.- 6.3. Neural Networks.- 6.3.1. An Introduction.- 6.3.2. Self-Organizing Kohonen Maps.- 6.3.3. Supervised Backpropagation Networks.- 6.3.4. Other Neural Network Models.- 7. Neural Network Applications.- 7.1. Introduction.- 7.2. Recognition of Distorted Characters.- 7.2.1. General Remarks.- 7.2.2. Matched Filtering.- 7.2.3. Error Probability for Binary Signals.- 7.2.4. Transmission and Discrimination of Characters...- 7.2.5. Results.- 7.3. Analysis and Visualization of Mutidimensional Remotely Sensed Image Data Sets.- 7.3.1. Remote Sensing Techniques.- 7.3.2. Cluster Visualization and Subspace Mapping.- 7.3.3. Studies on Satellite Image Classification.- 7.4. Interactive Identification and Reconstruction of Brain Tumors in MR-Images.- 7.4.1. Segmentation of Volume Data.- 7.4.2. Magnetic Resonance Technology.- 7.4.3. Clustering Texture Feature Spaces.- 7.4.4. Some Results.- 7.5. Automatic Face Recognition.- 7.5.1. Face Recognition Methods.- 7.5.2. Eigenfaces and Neural Networks.- 7.5.3. Results.- 7.5.4. Psychophysical Evidence.- 8. The Way Ahead.- Literature.