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» Efficient, Feature-based, Conditional Random Field Parsing
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BMVC
2002
13 years 9 months ago
Randomized RANSAC with T(d, d) test
Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant proportion of outliers. The RANSAC al...
Jiri Matas, Ondrej Chum
CVPR
2008
IEEE
14 years 8 months ago
Auto-context and its application to high-level vision tasks
The notion of using context information for solving highlevel vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context...
Zhuowen Tu
CVPR
2009
IEEE
15 years 1 months ago
Efficient Scale Space Auto-Context for Image Segmentation and Labeling
The Conditional Random Fields (CRF) model, using patch-based classification bound with context information, has recently been widely adopted for image segmentation/ labeling. In...
Jiayan Jiang (UCLA), Zhuowen Tu (UCLA)
CVPR
2010
IEEE
14 years 2 months ago
Automatic Discovery of Meaningful Object Parts with Latent CRFs
Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a...
Paul Schnitzspan, Stefan Roth, Bernt Schiele
ECCV
2010
Springer
13 years 7 months ago
Segmenting Salient Objects from Images and Videos
Abstract. In this paper we introduce a new salient object segmentation method, which is based on combining a saliency measure with a conditional random field (CRF) model. The propo...
Esa Rahtu, Juho Kannala, Mikko Salo, Janne Heikkil...