About: Theory of evidence has been successfully used in many areas covering pattern recognition and image processing due to its effectiveness in both information fusion and reasoning under uncertainty. Such notoriety led to extension of many existing Bayesian tools such as hidden Markov models, extensively used for image segmentation. This paper falls under this category of frameworks and aims to propose a new hidden Markov field that better handles nonGaussian forms of noise, designed for multichannel image segmentation. To this end, we use a recent kernel smoothing- based noise density estimation combined with a genuine approach of mass determination from data. The proposed model is validated on sampled and real remote sensing images and the results obtained outperform those produced by conventional hidden Markov fields.

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About: Theory of evidence has been successfully used in many areas covering pattern recognition and image processing due to its effectiveness in both information fusion and reasoning under uncertainty. Such notoriety led to extension of many existing Bayesian tools such as hidden Markov models, extensively used for image segmentation. This paper falls under this category of frameworks and aims to propose a new hidden Markov field that better handles nonGaussian forms of noise, designed for multichannel image segmentation. To this end, we use a recent kernel smoothing- based noise density estimation combined with a genuine approach of mass determination from data. The proposed model is validated on sampled and real remote sensing images and the results obtained outperform those produced by conventional hidden Markov fields. Goto Sponge NotDistinct Permalink

An Entity of Type : fabio:Abstract, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	abstract
value	Theory of evidence has been successfully used in many areas covering pattern recognition and image processing due to its effectiveness in both information fusion and reasoning under uncertainty. Such notoriety led to extension of many existing Bayesian tools such as hidden Markov models, extensively used for image segmentation. This paper falls under this category of frameworks and aims to propose a new hidden Markov field that better handles nonGaussian forms of noise, designed for multichannel image segmentation. To this end, we use a recent kernel smoothing- based noise density estimation combined with a genuine approach of mass determination from data. The proposed model is validated on sampled and real remote sensing images and the results obtained outperform those produced by conventional hidden Markov fields.
subject	Bioinformatics Image processing Machine learning Image segmentation Digital imaging »more»
part of	Dempster-Shafer Parzen-Rosenblatt Hidden Markov Fields for Multichannel Image Segmentation
is abstract of	Dempster-Shafer Parzen-Rosenblatt Hidden Markov Fields for Multichannel Image Segmentation
is hasSource of	covid:ann/target/5377a946853396ad1ec7b5510abce4480e93a3ad covid:ann/target/e3adf51b1c32545340851a11d84808bc73a9a888 covid:ann/target/7f519c949bf103055ca9770fd4056506386adff3 covid:ann/target/84d9fd6d85cc769f5e7bc2b4bab227777a6380b3 covid:ann/target/9842f8bf1dd9d4f7623276630f3f18d59b12f3d3 »more»

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