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David S. Cohen

dblp:32/5380 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 1995
—ORCID · unresolved

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

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 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
2 papers
Image recognition and object detection · 36% Representation and self-supervised learning · 28% Face, body and person analysis · 18%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › representation learning
feature extraction		 0.011992
Feature extraction from faces using deformable templates · Int. J. Comput. Vis. 1992
Computer vision › Image recognition and object detection › part-based model
deformable template		 0.011989
Feature extraction from faces using deformable templates · CVPR 1989
Computer vision › Image recognition and object detection › template matching
deformable template matching		 0.011989
Feature extraction from faces using deformable templates · CVPR 1989
Computer vision › Face, body and person analysis › face analysis
facial feature extraction		 0.011989
Feature extraction from faces using deformable templates · CVPR 1989
Computer vision › 3D vision › low-level vision
feature detection		 0.011989
Feature extraction from faces using deformable templates · CVPR 1989

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

deformable template · 0.0parameterized templates · 0.0energy minimization · 0.0
YearPublicationVenuePosition
1995 on the Problem of Sorting Burnt Pancakes
David S. Cohen, Manuel Blum 0001
 Discret. Appl. Math.1
1992 Feature extraction from faces using deformable templates
Alan L. Yuille, Peter W. Hallinan, David S. Cohen
 Int. J. Comput. Vis.3
1989 Feature extraction from faces using deformable templates
abstract
A method for detecting and describing the features of faces using deformable templates is described. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image, by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parameter values can be used as descriptors for the features. This method is demonstrated by showing deformable templates detecting eyes and mouths in real images.>
Alan L. Yuille, David S. Cohen, Peter W. Hallinan
 CVPR2