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» Covariant Derivatives and Vision
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CVPR
2005
IEEE
16 years 8 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
ICCV
2007
IEEE
16 years 8 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
ICCV
2003
IEEE
16 years 8 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
ICCV
2003
IEEE
16 years 8 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
ECCV
2008
Springer
16 years 8 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