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Michael Brockington

dblp:45/450 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1993
—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 · 2

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
2 papers
Image and video processing · 42% Computational photography and imaging · 37% Geometric modeling and processing · 21%

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

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › surface parameterization
conformal mapping
0.011993
Conformal transplantation of lightness to varying resolution sensors · CVPR 1993
Image and video processing › color image processing
lightness computation
0.011993
Conformal transplantation of lightness to varying resolution sensors · CVPR 1993
Image and video processing › image enhancement
retinex
0.011993
Conformal transplantation of lightness to varying resolution sensors · CVPR 1993
Computational photography and imaging
intrinsic image decomposition
0.011992
Recovering Shading from Color Images · ECCV 1992
Computational photography and imaging › intrinsic image decomposition
shading recovery
0.011992
Recovering Shading from Color Images · ECCV 1992

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

varying resolution sensor · 0.0conformal transplant · 0.0
YearPublicationVenuePosition
1993 Conformal transplantation of lightness to varying resolution sensors
abstract
Many standard computer vision algorithms for shape from shading, optical flow, color constancy or surface reconstruction depend upon the solution of Poisson equations. Using retinex lightness computation as an example, it is shown how these algorithms can be dramatically sped up using a varying resolution sensor, so long as the sensor architecture is based on a conformal mapping. The varying resolution lightness algorithm is developed by the technique of using a conformal transplant. In the final analysis, very little of the algorithm needs to be changed, but a speed up in running time of roughly 50 times is obtained, even though the total amount of data is reduced by only a factor of 28. The speed increases at the expense of peripheral resolution.>
Brian V. Funt, Michael Brockington
CVPR2
1992 Recovering Shading from Color Images
Brian V. Funt, Mark S. Drew, Michael Brockington
ECCV3