Title :
A Markov random field model for object matching under contextual constraints
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
Abstract :
This paper presents a Markov random field (MRF) model for object recognition in high level vision. The labeling state of a scene in terms of a model object is considered as an MRF or couples MRFs. Within the Bayesian framework the optimal solution is defined as the maximum a posteriori (MAP) estimate of the MRF. The posterior distribution is derived based on sound mathematical principles from theories of MRF and probability, which is in contrast to heuristic formulations. An experimental result is presented
Keywords :
Markov processes; computer vision; image sequences; probability; Bayesian framework; MRF; Markov random field; contextual constraints; high level vision; maximum a posteriori; object matching; object recognition; probability; Image matching; Machine vision; Markov processes; Object recognition; Probability;
Conference_Titel :
Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-8186-5825-8
DOI :
10.1109/CVPR.1994.323915
