Visual Computing: The Integration of Computer Graphics, Visual Perception and Imaging

Visual Computing: The Integration of Computer Graphics, Visual Perception and Imaging

by Markus Groß

Paperback(Softcover reprint of the original 1st ed. 1994)

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Advances in computing and communications have brought about an increasing demand for visual information. Visual Computing addresses the principles behind "visual technology", and provides readers with a good understanding of how the integration of Computer Graphics, Visual Perception and Imaging is achieved.
Included in the book is an overview of important research areas within this integration which will be useful for further work in the field. Foundations of visual perception and psychophysics are presented as well as basic methods of imaging and computer vision.
This book serves as an excellent reference and textbook for those who wish to apply or study "visual computing technology."

Product Details

ISBN-13: 9783642850257
Publisher: Springer Berlin Heidelberg
Publication date: 01/19/2012
Series: Computer Graphics: Systems and Applications
Edition description: Softcover reprint of the original 1st ed. 1994
Pages: 334
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

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.

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