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Adaptive Segmentation of Images of Objects with Smooth Surfaces

George K. Gregoriou, Amir Waks, Oleh J. Tretiak
CVPR 1993 pp. 772-773
doi:10.1109/CVPR.1993.341168 /cvpr/1993/gregoriou1993cvpr-adaptive/

Abstract

The problem of adaptive segmentation of images of objects with smooth surfaces is addressed. The images are composed of regions of slowly varying intensities that may be corrupted by additive noise. The underlying field is modeled by Markov random field that consists of both a label process which contains the classification of each pixel in the image and intensity functions which contain the possible grey levels that each pixel may take. The algorithm iteratively repeats two steps: the parameter estimation step, in which the maximum-likelihood (ML) estimates of the associated parameters are obtained; and the restoration step, in which the underlying field is estimated through the maximum-a-posteriori (MAP) method. The concept of allowing the pixel grey values to vary across the image regions is discussed. These values are estimated by using windows on the observed data. As the algorithm progresses, the window size is decreased so that the algorithm adapts to the characteristics of each region.< <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

Gregoriou et al. "Adaptive Segmentation of Images of Objects with Smooth Surfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.341168

Markdown

[Gregoriou et al. "Adaptive Segmentation of Images of Objects with Smooth Surfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/gregoriou1993cvpr-adaptive/) doi:10.1109/CVPR.1993.341168

BibTeX

@inproceedings{gregoriou1993cvpr-adaptive,
  title     = {{Adaptive Segmentation of Images of Objects with Smooth Surfaces}},
  author    = {Gregoriou, George K. and Waks, Amir and Tretiak, Oleh J.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1993},
  pages     = {772-773},
  doi       = {10.1109/CVPR.1993.341168},
  url       = {https://mlanthology.org/cvpr/1993/gregoriou1993cvpr-adaptive/}
}