Applications of hidden Markov chains in image analysis
Identifieur interne : 001F22 ( Main/Exploration ); précédent : 001F21; suivant : 001F23Applications of hidden Markov chains in image analysis
Auteurs : Kjersti Aas [Norvège] ; Line Eikvil [Norvège] ; Ragnar Bang Huseby [Norvège]Source :
- Pattern Recognition [ 0031-3203 ] ; 1998.
Abstract
In image analysis, two-dimensional Markov models, i.e. Markov field models, have been applied for segmentation purposes, but except for the area of text recognition, the application of hidden Markov chains has been rare. Through four very different examples, this paper demonstrates the applicability also for hidden Markov chains in image analysis, and shows that the problems of image analysis often may have one- dimensional characteristics even though the images are two-dimensional.
Url:
DOI: 10.1016/S0031-3203(98)00109-5
Affiliations:
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<front><div type="abstract" xml:lang="en">In image analysis, two-dimensional Markov models, i.e. Markov field models, have been applied for segmentation purposes, but except for the area of text recognition, the application of hidden Markov chains has been rare. Through four very different examples, this paper demonstrates the applicability also for hidden Markov chains in image analysis, and shows that the problems of image analysis often may have one- dimensional characteristics even though the images are two-dimensional.</div>
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