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ICCV
2003
IEEE
16 years 1 months ago
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the i...
Marshall F. Tappen, William T. Freeman
ICIP
2008
IEEE
16 years 1 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
MICCAI
2002
Springer
16 years 22 days ago
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm att...
David T. Gering, W. Eric L. Grimson, Ron Kikinis
ICML
2009
IEEE
16 years 20 days ago
Learning structurally consistent undirected probabilistic graphical models
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
PAMI
2007
176views more  PAMI 2007»
14 years 11 months ago
Approximate Labeling via Graph Cuts Based on Linear Programming
A new framework is presented for both understanding and developing graph-cut based combinatorial algorithms suitable for the approximate optimization of a very wide class of MRFs ...
Nikos Komodakis, Georgios Tziritas