ML Anthology

Contagion-Based Image Segmentation and Labeling

Amit Banerjee, Philippe Burlina, Fady Alajaji
ICCV 1998 pp. 255-260
doi:10.1109/ICCV.1998.710727 /iccv/1998/banerjee1998iccv-contagion/

Abstract

We propose a segmentation method based on Polya's urn model for contagious phenomena. Initial labeling of the pixel is obtained using a Maximum Likelihood (ML) estimate or the Nearest Mean Classifier (NMC), which are used to determine the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. Examples of the application of this scheme to the segmentation of synthetic texture images, Ultra-Wideband Synthetic Aperture Radar (UWB SAR) images and Magnetic Resonance Images (MRI) are provided.

ICCV Semantic Scholar

Cite

Text

Banerjee et al. "Contagion-Based Image Segmentation and Labeling." IEEE/CVF International Conference on Computer Vision, 1998. doi:10.1109/ICCV.1998.710727

Markdown

[Banerjee et al. "Contagion-Based Image Segmentation and Labeling." IEEE/CVF International Conference on Computer Vision, 1998.](https://mlanthology.org/iccv/1998/banerjee1998iccv-contagion/) doi:10.1109/ICCV.1998.710727

BibTeX

@inproceedings{banerjee1998iccv-contagion,
  title     = {{Contagion-Based Image Segmentation and Labeling}},
  author    = {Banerjee, Amit and Burlina, Philippe and Alajaji, Fady},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1998},
  pages     = {255-260},
  doi       = {10.1109/ICCV.1998.710727},
  url       = {https://mlanthology.org/iccv/1998/banerjee1998iccv-contagion/}
}