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ECCV
2008
Springer
14 years 11 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
14 years 11 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
VIS
2003
IEEE
234views Visualization» more  VIS 2003»
14 years 11 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
14 years 10 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
14 years 10 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...