DocumentCode :
2241319
Title :
Adaptive segmentation of images of objects with smooth surfaces
Author :
Gregoriou, George K. ; Waks, Amir ; Tretiak, Oleh J.
Author_Institution :
ECE Dept., Drexel Univ., Philadelphia, PA, USA
fYear :
1993
fDate :
15-17 Jun 1993
Firstpage :
772
Lastpage :
773
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
Keywords :
Markov processes; adaptive signal processing; image classification; image restoration; image segmentation; iterative methods; maximum likelihood estimation; Markov random field; adaptive segmentation; additive noise; grey levels; image segmentation; intensity functions; iterative algorithm; label process; maximum a posteriori method; maximum-likelihood estimates; parameter estimation; pixel classification; restoration step; slowly varying intensities; smooth surfaces; Additive noise; Computer vision; Image restoration; Image segmentation; Iterative algorithms; Markov random fields; Maximum likelihood estimation; Parameter estimation; Pixel; Stochastic resonance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1993. Proceedings CVPR '93., 1993 IEEE Computer Society Conference on
Conference_Location :
New York, NY
ISSN :
1063-6919
Print_ISBN :
0-8186-3880-X
Type :
conf
DOI :
10.1109/CVPR.1993.341168
Filename :
341168
Link To Document :
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