Stochastic image segmentation using spatial-temporal context

A stochastic syntactic method for the analysis of time-varying images is presented. The time-varying phenomenon is analyzed using a language translation schema. Stochastic tree grammars are used to describe the patterns, and the stochastic translation is used to characterize the evolution process of...

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Main Author: Don Hon-Son
Format: Conference Proceeding
Language:English
Subjects:
Context modeling
Data mining
Equations
Image segmentation
Image sequence analysis
Matched filters
Pattern recognition
Production
Stochastic processes
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description A stochastic syntactic method for the analysis of time-varying images is presented. The time-varying phenomenon is analyzed using a language translation schema. Stochastic tree grammars are used to describe the patterns, and the stochastic translation is used to characterize the evolution process of the time-varying image sequence. It is shown that the necessary conditions for the existence of a matched filter are preserved under the stochastic translation. Therefore, a spatial filter can be designed for the patterns at each stage of the image sequence, and a temporal filter can be designed for the trajectories formed by the pattern primitives. These filters can iteratively extract the contextual information in a pattern. Emphasis is on the relationships between the filters.< >
doi_str_mv 10.1109/ICPR.1988.28237
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ispartof [1988 Proceedings] 9th International Conference on Pattern Recognition, 1988, p.342-344 vol.1
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Context modeling
Data mining
Equations
Image segmentation
Image sequence analysis
Matched filters
Pattern recognition
Production
Stochastic processes
title Stochastic image segmentation using spatial-temporal context
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