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» A Conditional Random Field Model for Video Super-resolution
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TOG
2008
293views more  TOG 2008»
13 years 7 months ago
A perceptually validated model for surface depth hallucination
Capturing detailed surface geometry currently requires specialized equipment such as laser range scanners, which despite their high accuracy, leave gaps in the surfaces that must ...
Mashhuda Glencross, Gregory J. Ward, Francho Melen...
CVPR
2009
IEEE
15 years 2 months ago
Alphabet SOUP: A Framework for Approximate Energy Minimization
Many problems in computer vision can be modeled using conditional Markov random fields (CRF). Since finding the maximum a posteriori (MAP) solution in such models is NP-hard, mu...
Stephen Gould (Stanford University), Fernando Amat...
ECCV
2006
Springer
14 years 9 months ago
Learning and Incorporating Top-Down Cues in Image Segmentation
Abstract. Bottom-up approaches, which rely mainly on continuity principles, are often insufficient to form accurate segments in natural images. In order to improve performance, rec...
Xuming He, Richard S. Zemel, Debajyoti Ray
ACL
2007
13 years 8 months ago
A Unified Tagging Approach to Text Normalization
This paper addresses the issue of text normalization, an important yet often overlooked problem in natural language processing. By text normalization, we mean converting ‘inform...
Conghui Zhu, Jie Tang, Hang Li, Hwee Tou Ng, Tieju...
ACL
2010
13 years 5 months ago
Practical Very Large Scale CRFs
Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate str...
Thomas Lavergne, Olivier Cappé, Franç...