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Kevin M. A. de Souza

dblp:94/5312 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1997
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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.

Artificial intelligence
1 paper
Image recognition and object detection · 91% Probabilistic and Bayesian machine learning · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › template matching
deformable template matching		 0.011997
Deformable Template Recognition of Multiple Occluded Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1997
Computer vision › Image recognition and object detection
object recognition		 0.011997
Deformable Template Recognition of Multiple Occluded Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1997
Computer vision › Image recognition and object detection › object recognition › robust object recognition
occluded object recognition		 0.011997
Deformable Template Recognition of Multiple Occluded Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1997
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
bayesian inference		 0.011997
Deformable Template Recognition of Multiple Occluded Objects · IEEE Trans. Pattern Anal. Mach. Intell. 1997

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

deformable template · 0.0bayesian modeling · 0.0
YearPublicationVenuePosition
1997 Estimation of Objects in Highly Variable Images Using Markov Chain Monte Carlo
Kevin M. A. de Souza, John T. Kent, Kanti V. Mardia
 BMVC1
1997 Deformable Template Recognition of Multiple Occluded Objects
abstract
Based on deformable templates, the paper formulates an integrated and flexible Bayesian recognition system of multiple occluded objects. Various local dependence properties of the model are obtained to reduce the computational cost with the increase in the number of objects. Numerical results for a synthetic image and for a real image of mushrooms are discussed.
Kanti V. Mardia, Kevin M. A. de Souza
 IEEE Trans. Pattern Anal. Mach. Intell.3