Conference proceeding
Adaptive segmentation of images of objects with smooth surfaces
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 772-773
1993
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.< >
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Details
- Title
- Adaptive segmentation of images of objects with smooth surfaces
- Creators
- G.K. Gregoriou - Drexel UniversityA. WaksO.J. Tretiak - Drexel UniversityIEEE, COMP SOC
- Publication Details
- Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 772-773
- Publisher
- IEEE Comput. Soc. Press
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:A1993BY86V00173
- Other Identifier
- 991021965460704721
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- Web of Science research areas
- Engineering, Electrical & Electronic