1. Introduction. - 1. 0 Overview of spatial vision. - 1. 1 Two viewpoints on computer vision. - 1. 2 A demonstration of perceptual organization. - 1. 3 Specific functions of perceptual organization. - 2. Previous Research. - 2. 1 Gestalt psychology and perceptual organization. - 2. 2 The principle of simplicity. - 2. 3 Grouping as the formation of causal relations. - 2. 4 The role of grouping in computer vision systems. - 3. The Significance of Image Relations. - 3. 1 Probability of accidental occurrence. - 3. 2 Limiting computational complexity. - 3. 3 Conclusions. - 4. The Segmentation of Image Curves. - 4. 1 Previous research on curve segmentation. - 4. 2 Significance of a curve segmentation. - 4. 3 Selecting the most significant structures. - 4. 4 Demonstration of the algorithm. - 4. 5 Evaluation and future research. - 5. The Use of Viewpoint Invariance. - 5. 1 Three-space inferences from image features. - 5. 2 Recovery of 3D properties from line drawings. - 5. 3 A demonstration of three-space inference. - 5. 4 Conclusions and future development. - 6. Model-based Search and Inference. - 6. 1 Searching the space of possible viewpoints. - 6. 2 Searching the space of possible objects. - 6. 3 Summary. - 7. The Verification of Interpretations. - 7. 1 Viewpoint determination in human vision. - 7. 2 Definition of the problem. - 7. 3 Previous research on viewpoint determination. - 7. 4 Formulation of perspective projection. - 7. 5 Newton-Raphson convergence. - 7. 6 Solving for model parameters. - 7. 7 Matching lines instead of points. - 7. 8 Implementation and future research. - 8. The Scerpo Vision System. - 8. 1 Edge detection. - 8. 2 Perceptual organization. - 8. 3 Matching and evidential reasoning. - 8. 4 Verification. - 8. 5 System performance and future extensions. - 9. Conclusions. - 9. 1 Directions for future development. - Bibliographic Index. Language: English