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» Coupled hidden Markov models for complex action recognition
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ICCV
2003
IEEE
14 years 9 months ago
Recognition of Group Activities using Dynamic Probabilistic Networks
Dynamic Probabilistic Networks (DPNs) are exploited for modelling the temporal relationships among a set of different object temporal events in the scene for a coherent and robust...
Shaogang Gong, Tao Xiang
CVPR
2006
IEEE
14 years 9 months ago
Hidden Conditional Random Fields for Gesture Recognition
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorpo...
Sy Bor Wang, Ariadna Quattoni, Louis-Philippe More...
CVPR
2004
IEEE
13 years 11 months ago
Modeling Complex Motion by Tracking and Editing Hidden Markov Graphs
In this paper, we propose a generative model for representing complex motion, such as wavy river, dancing fire and dangling cloth. Our generative method consists of four component...
Yizhou Wang, Song Chun Zhu
PAMI
2006
109views more  PAMI 2006»
13 years 7 months ago
Offline Grammar-Based Recognition of Handwritten Sentences
This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Sto...
Matthias Zimmermann, Jean-Cédric Chappelier...
HICSS
2003
IEEE
220views Biometrics» more  HICSS 2003»
14 years 20 days ago
Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks
This paper describes a novel approach using Hidden Markov Models (HMM) to detect complex Internet attacks. These attacks consist of several steps that may occur over an extended pe...
Dirk Ourston, Sara Matzner, William Stump, Bryan H...