ML Anthology

Model Based Region Segmentation Using Cooccurrence Matrices

Stéphane Houzelle, Gérard Giraudon
CVPR 1992 pp. 636-639
doi:10.1109/CVPR.1992.223121 /cvpr/1992/houzelle1992cvpr-model/

Abstract

A region segmentation algorithm is presented, using a model for joint probability density. Joint probability density can be defined as an N*N cooccurrence matrix in which each coordinate (i, j) gives the probability for the gray-level transition i, j between two neighbor pixels. The approach consists in modeling the energy distribution within a cooccurrence matrix of a region. Regions are assumed to be stationary. A region-growing scheme that proceeds in two steps is used. The first step consists of learning the parameters of the model. The second step is the segmentation process. Starting with a seed pixel, new pixels are incorporated in the region if their neighborhoods fit the model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Houzelle and Giraudon. "Model Based Region Segmentation Using Cooccurrence Matrices." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992. doi:10.1109/CVPR.1992.223121

Markdown

[Houzelle and Giraudon. "Model Based Region Segmentation Using Cooccurrence Matrices." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992.](https://mlanthology.org/cvpr/1992/houzelle1992cvpr-model/) doi:10.1109/CVPR.1992.223121

BibTeX

@inproceedings{houzelle1992cvpr-model,
  title     = {{Model Based Region Segmentation Using Cooccurrence Matrices}},
  author    = {Houzelle, Stéphane and Giraudon, Gérard},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1992},
  pages     = {636-639},
  doi       = {10.1109/CVPR.1992.223121},
  url       = {https://mlanthology.org/cvpr/1992/houzelle1992cvpr-model/}
}