A Markov Random Field Model for Object Matching Under Contextual Constraints

Li, Stan Z.

doi:10.1109/CVPR.1994.323915

A Markov Random Field Model for Object Matching Under Contextual Constraints

Stan Z. Li

CVPR 1994 pp. 866-869

doi:10.1109/CVPR.1994.323915 /cvpr/1994/li1994cvpr-markov/

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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Cite

Text

Li. "A Markov Random Field Model for Object Matching Under Contextual Constraints." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994. doi:10.1109/CVPR.1994.323915

Markdown

[Li. "A Markov Random Field Model for Object Matching Under Contextual Constraints." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994.](https://mlanthology.org/cvpr/1994/li1994cvpr-markov/) doi:10.1109/CVPR.1994.323915

BibTeX

@inproceedings{li1994cvpr-markov,
  title     = {{A Markov Random Field Model for Object Matching Under Contextual Constraints}},
  author    = {Li, Stan Z.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1994},
  pages     = {866-869},
  doi       = {10.1109/CVPR.1994.323915},
  url       = {https://mlanthology.org/cvpr/1994/li1994cvpr-markov/}
}