DocumentCode :
2557161
Title :
A uniform Bayesian framework for integration
Author :
Pankanti, Sharath ; Jain, Anil K.
Author_Institution :
Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA
fYear :
1995
fDate :
21-23 Nov 1995
Firstpage :
497
Lastpage :
502
Abstract :
Vision researchers have advocated the integration of vision modules. However, generic system integration issues for recovering 3D information have not been adequately addressed in the literature. The authors propose a unified Bayesian integration framework for interactions among the vision modules to obtain a complete 3D reconstruction from a pair of intensity (stereo) images. The authors have integrated perceptual grouping, stereo, shape from shading, and shape from texture modules under the proposed framework. They have demonstrated that the integrated system recovers the depth and surface orientation information more reliably than the individual modules for different synthetic and real images
Keywords :
Bayes methods; image reconstruction; image texture; probability; stereo image processing; 3D information recovery; complete 3D reconstruction; intensity images; perceptual grouping; real images; shape from shading; shape from texture; stereo images; synthetic images; uniform Bayesian framework; vision modules; Bayesian methods; Computer science; Computer vision; Image reconstruction; Layout; Modular construction; Reflectivity; Shape; Stereo vision; Surface reconstruction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 1995. Proceedings., International Symposium on
Conference_Location :
Coral Gables, FL
Print_ISBN :
0-8186-7190-4
Type :
conf
DOI :
10.1109/ISCV.1995.477050
Filename :
477050
Link To Document :
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