The following topics are dealt with: edge and boundary analysis;vision systems;motion;shape and 2-D description;stereo and 3-D description;patternrecognition;3-D models;architectures;vision models and texture;image s...
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ISBN:
(纸本)0818606339
The following topics are dealt with: edge and boundary analysis;vision systems;motion;shape and 2-D description;stereo and 3-D description;patternrecognition;3-D models;architectures;vision models and texture;image segmentation;applications and parallel algorithms;3-D analysis;contour analysis;character recognition;3-D descriptions from multiple views;and parallel architectures for imageprocessing. 123 papers were presented, of which 121 are published in full in the present proceedings.
The Bayesian segmentation model developed is motivated by consideration of the information needed for higher-level visual processing. A segmentation is regarded as a collection of parameters defining an image-valued s...
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ISBN:
(纸本)0818621486
The Bayesian segmentation model developed is motivated by consideration of the information needed for higher-level visual processing. A segmentation is regarded as a collection of parameters defining an image-valued stochastic process by separating topological (adjacency) and metric (shape) properties of the subdivision and intensity properties of each region. The prior selection is structured accordingly. The novel part of the representation, the subdivision topology, is assigned a prior by universal coding arguments, using the minimum description-length philosophy that the best segmentation allows the most efficient representation of visual data.< >
This work considers the problem of discovering areas of convergence of line-like shapes in an image. The motivating application is to use the convergence of the blood vessel network to automatically locate the optic n...
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ISBN:
(纸本)0818684976
This work considers the problem of discovering areas of convergence of line-like shapes in an image. The motivating application is to use the convergence of the blood vessel network to automatically locate the optic nerve in an ocular fundus image. A fuzzy segment model is proposed, based on a conjecture that line-like shapes only contribute to a perception of convergence in their near neighborhood. Using this model, a voting-type method is described to compute a convergence image, which can be searched for one absolute, or one or more relative, strongest points of convergence. Results are presented for twenty ocular fundus images, with a 65% success rate for finding the optic nerve.
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