Visual Computing

The future of computing and communication will apply visual information to a much larger degree than what we are used to, or even what we envisage today. Visual computing addresses the integration of "visual technology" and provides a concept for a common understanding of Computer Graphics, Visual Perception and Imaging. The goal of this book is to present an overview on important topics within this integration in the spirit of recent worldwide research and development. Foundations of visual perception and of psychophysics are presented as well as basic methods of imaging and computer vision. The book serves as a reference and textbook for those who wish to apply or study "visual computing technology".

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.