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» Covariant Derivatives and Vision
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93
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CVPR
2005
IEEE
16 years 2 months ago
Energy Minimization via Graph Cuts: Settling What is Possible
The recent explosion of interest in graph cut methods in computer vision naturally spawns the question: what energy functions can be minimized via graph cuts? This question was fi...
Daniel Freedman, Petros Drineas
87
Voted
ICCV
2007
IEEE
16 years 2 months ago
A Matte-less, Variational Approach to Automatic Scene Compositing
In this paper, we consider the problem of compositing a scene from multiple images. Multiple images, for example, can be obtained by varying the exposure of the camera, by changin...
Shanmuganathan Raman, Subhasis Chaudhuri
85
Voted
ICCV
2003
IEEE
16 years 2 months ago
Robust Regression with Projection Based M-estimators
The robust regression techniques in the RANSAC family are popular today in computer vision, but their performance depends on a user supplied threshold. We eliminate this drawback ...
Haifeng Chen, Peter Meer
119
Voted
ICCV
2003
IEEE
16 years 2 months ago
On the Use of Marginal Statistics of Subband Images
A commonly used representation of a visual pattern is the set of marginal probability distributions of the output of a bank of filters (Gaussian, Laplacian, Gabor etc...). This re...
Joshua Gluckman
88
Voted
ECCV
2008
Springer
16 years 2 months ago
Surface Visibility Probabilities in 3D Cluttered Scenes
Many methods for 3D reconstruction in computer vision rely on probability models, for example, Bayesian reasoning. Here we introduce a probability model of surface visibilities in ...
Michael S. Langer