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Russell Epstein

dblp:71/249 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

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 · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 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.

Artificial intelligence
1 paper
3D vision · 50% Video understanding and tracking · 50%
Computer graphics and multimedia
1 paper
Rendering · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking › object tracking › appearance modeling
illumination modeling
0.011999
Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability · Int. J. Comput. Vis. 1999
Computer vision › 3D vision › inverse rendering
shape and albedo estimation
0.011999
Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability · Int. J. Comput. Vis. 1999
Rendering › appearance modeling › reflectance and appearance modeling
reflectance and illumination modeling
0.011999
Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability · Int. J. Comput. Vis. 1999

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

singular value decomposition · 0.0integrability · 0.0
YearPublicationVenuePosition
2011 Finding your way: The cognitive science of navigation
Benjamin Kuipers, Neil Burgess, Russell Epstein, Verner P. Bingman, Francesca Pazzaglia
CogSci3
1999 Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability
Alan L. Yuille, Daniel Snow, Russell Epstein, Peter N. Belhumeur
Int. J. Comput. Vis.3
1994 Training a General Purpose Deformable Template
abstract
We propose a general purpose deformable template that can be used to recognize images of different classes of objects. We demonstrate a method by which prior information about a class of objects can be systematically incorporated into the template to form a model for the class. This is done by having two separate levels of parameterization: a set of prior variables whose values specify a prior model, and a set of configuration variables which can be varied to match a model to a specific image. When trained on a set of faces, the template successfully distinguished between face and non-face, as well as between different faces.>
Russell Epstein, Alan L. Yuille
ICIP (1)1