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ECCV
2008
Springer
16 years 2 months ago
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
Abstract. Recently, impressive results have been reported for the detection of objects in challenging real-world scenes. Interestingly however, the underlying models vary greatly e...
Paul Schnitzspan, Mario Fritz, Bernt Schiele
ICIP
2007
IEEE
16 years 2 months ago
Automatic Parametrisation for an Image Completion Method Based on Markov Random Fields
Recently, a new exemplar-based method for image completion, texture synthesis and image inpainting was proposed which uses a discrete global optimization strategy based on Markov ...
Huy Tho Ho, Roland Göcke
97
Voted
VIS
2003
IEEE
234views Visualization» more  VIS 2003»
16 years 2 months ago
Saddle Connectors - An Approach to Visualizing the Topological Skeleton of Complex 3D Vector Fields
One of the reasons that topological methods have a limited popularity for the visualization of complex 3D flow fields is the fact that such topological structures contain a number...
Hans-Christian Hege, Hans-Peter Seidel, Holger The...
ICML
2005
IEEE
16 years 1 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
ICML
2004
IEEE
16 years 1 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...