About: Dempster-Shafer Parzen-Rosenblatt Hidden Markov Fields for Multichannel Image Segmentation

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Dempster-Shafer Parzen-Rosenblatt Hidden Markov Fields for Multichannel Image Segmentation
Creator	Debicha, Islam Hamache, Ali Sadouk, Hamza El, Mohamed Boudaren, Yazid
source	PMC
abstract	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.
has issue date	2020-05-18(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50146-4_45
has license	no-cc
sha1sum (hex)	19a21cafd97ce2671dc07f47f85e1c2dba2b3332
schema:url	https://doi.org/10.1007/978-3-030-50146-4_45
resource representing a document's title	Dempster-Shafer Parzen-Rosenblatt Hidden Markov Fields for Multichannel Image Segmentation
has PubMed Central identifier	PMC7274318
schema:publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems
resource representing a document's body	covid:19a21cafd97ce2671dc07f47f85e1c2dba2b3332#body_text
is schema:about of	named entity 'aims' named entity 'validated' named entity 'images' named entity 'Theory' named entity 'CONVENTIONAL' »more»

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