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

Mohamed El Yazid Boudaren; Ali Hamache; Islam Debicha; Hamza Tarik Sadouk

doi:10.1007/978-3-030-50146-4_45

ScienceGate Book Chapters

BOOK-CHAPTER

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

Mohamed El Yazid Boudaren Ali Hamache Islam Debicha Hamza Tarik Sadouk

Year: 2020 Communications in computer and information science Pages: 613-624 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-50146-4_45

Get Full-Text PDF Get Analytical Report

Keywords:

Artificial intelligence Hidden Markov model Computer science Pattern recognition (psychology) Kernel density estimation Segmentation Smoothing Image segmentation Markov chain Bayesian probability Kernel (algebra) Markov model Markov random field Computer vision Machine learning Mathematics Statistics

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.13

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Advanced Image and Video Retrieval Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Automated Road and Building Extraction

Physical Sciences → Engineering → Ocean Engineering

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

Metrics

Topics

Related Documents

Multisensor image segmentation using Dempster-Shafer fusion in Markov fields context

Dempster–Shafer fusion of evidential pairwise Markov fields

Pairwise and Hidden Markov Random Fields in Image Segmentation

Study of Dempster–Shafer theory for image segmentation applications

Color image segmentation based on Dempster-Shafer evidence theory