Title :
Stochastic image segmentation using spatial-temporal context
Author_Institution :
Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA
Abstract :
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
Keywords :
filtering and prediction theory; grammars; pattern recognition; picture processing; spatial filters; stochastic processes; trees (mathematics); image segmentation; iterative information extraction; language translation schema; matched filter; pattern primitives; pattern recognition; picture processing; spatial filter; spatial-temporal context; stochastic syntactic method; time-varying images; tree grammars; Context modeling; Data mining; Equations; Image segmentation; Image sequence analysis; Matched filters; Pattern recognition; Production; Stochastic processes; TV;
Conference_Titel :
Pattern Recognition, 1988., 9th International Conference on
Conference_Location :
Rome
Print_ISBN :
0-8186-0878-1
DOI :
10.1109/ICPR.1988.28237
