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Anjan Banerjee

dblp:71/6960 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 33% Multimedia analysis and retrieval · 33% Virtual and augmented reality · 33%
Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models		 0.112005
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery · IEEE Trans. Image Process. 2005
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models
markov random field		 0.112005
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery · IEEE Trans. Image Process. 2005
Image and video processing
image segmentation		 0.112005
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery · IEEE Trans. Image Process. 2005
Multimedia analysis and retrieval › image classification
land cover classification		 0.112005
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery · IEEE Trans. Image Process. 2005
Virtual and augmented reality › tracking
sensor fusion		 0.112005
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery · IEEE Trans. Image Process. 2005

Methods — techniques the papers use, named apart from their topics

markov random field · 0.1dempster-shafer theory · 0.1
YearPublicationVenuePosition
2015 Adapting RANSAC SVM to Detect Outliers for Robust Classification
Subhabrata Debnath, Anjan Banerjee, Vinay P. Namboodiri
 BMVC2
2005 Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery
abstract
This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.
Anjan Sarkar, Anjan Banerjee, Nilanjan Banerjee, Siddhartha Brahma, B. Kartikeyan, Manab Chakraborty, Kantilal L. Majumder
 IEEE Trans. Image Process.2