Perceptual Organization and Visual Recognition

1. Introduction.- 1.0 Overview of spatial vision.- 1.0.1 Viewpoint and spatial correspondence.- 1.0.2 Perceptual organization.- 1.0.3 The relationship to psychology.- 1.1 Two viewpoints on computer vision.- 1.1.1 Production of disambiguated representations.- 1.1.2 Searching for spatial correspondence.- 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.- 3.1.1 Viewpoint invariance conditions.- 3.1.2 Prior knowledge of probability of occurrence.- 3.1.3 Null hypothesis of position independence.- 3.1.4 Ratio of background density to proximity.- 3.1.5 Recursive application of structuring.- 3.2 Limiting computational complexity.- 3.2.1 Consider only local neighborhoods.- 3.2.2 Texture characterizations.- 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.- 6.1.1 The role of perceptual organization.- 6.2 Searching the space of possible objects.- 6.2.1 A model of probabilistic search.- 6.2.2 Evidential reasoning for reducing search.- 6.2.3 Evidential reasoning in vision.- 6.3 Summary.- 7. The Verification of Interpretations.- 7.1 Viewpoint determination in human vision.- 7.2 Definition of the problem.- 7.3 Previous