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» Experimental Evaluation of Hierarchical Hidden Markov Models
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PAKDD
2009
ACM
171views Data Mining» more  PAKDD 2009»
15 years 6 months ago
Detecting Abnormal Events via Hierarchical Dirichlet Processes
Abstract. Detecting abnormal event from video sequences is an important problem in computer vision and pattern recognition and a large number of algorithms have been devised to tac...
Xian-Xing Zhang, Hua Liu, Yang Gao, Derek Hao Hu
ICIP
2008
IEEE
16 years 3 months ago
Activity-based temporal segmentation for videos of interacting objects using invariant trajectory features
This paper presents a content-based approach for temporal segmentation of videos. Tracked objects are characterized by their 2D trajectories which are used in a meaningful way to ...
Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre L...
SSPR
2004
Springer
15 years 7 months ago
Automatic Labeling of Sports Video Using Umpire Gesture Recognition
We present results on an extension to our approach for automatic sports video annotation. Sports video is augmented with accelerometer data from wrist bands worn by umpires in the ...
Graeme S. Chambers, Svetha Venkatesh, Geoff A. W. ...
ICIP
2000
IEEE
16 years 3 months ago
Hierarchical Image Probability (HIP) Models
We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vecto...
Clay Spence, Lucas C. Parra, Paul Sajda
ECAI
2006
Springer
15 years 5 months ago
Polynomial Conditional Random Fields for Signal Processing
We describe Polynomial Conditional Random Fields for signal processing tasks. It is a hybrid model that combines the ability of Polynomial Hidden Markov models for modeling complex...
Trinh Minh Tri Do, Thierry Artières