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» Modeling High-Order Dependencies in Local Appearance Models
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ICCV
2007
IEEE
15 years 10 months ago
Scale-Dependent 3D Geometric Features
Three-dimensional geometric data play fundamental roles in many computer vision applications. However, their scale-dependent nature, i.e. the relative variation in the spatial ext...
John Novatnack, Ko Nishino
EMNLP
2006
15 years 5 months ago
A Skip-Chain Conditional Random Field for Ranking Meeting Utterances by Importance
We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies bet...
Michel Galley
INFOCOM
2003
IEEE
15 years 9 months ago
Asymptotic Insensitivity of Least-Recently-Used Caching to Statistical Dependency
Abstract— We investigate a widely popular Least-RecentlyUsed (LRU) cache replacement algorithm with semi-Markov modulated requests. Semi-Markov processes provide the flexibility...
Predrag R. Jelenkovic, Ana Radovanovic
IJCNN
2006
IEEE
15 years 10 months ago
Training Reformulated Product Units in Hybrid Neural Networks
— Higher order networks allow modelling of correlates and geometrically invariant properties. Current techniques for their development either require domain knowledge, or are con...
Philip T. Elliott, Diven Topiwala, Will N. Browne
113
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ICPR
2004
IEEE
16 years 5 months ago
Image Segmentation by Shape Particle Filtering
Statistical appearance models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but can not cope with local appe...
Mads Nielsen, Marleen de Bruijne