Fundamentals of speech recognition
Fundamentals of speech recognition
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Vision-Based Gesture Recognition: A Review
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Recognition of human body motion using phase space constraints
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Unsupervised clustering of ambulatory audio and video
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Similarity-based analysis for large networks of ultra-low resolution sensors
Pattern Recognition
Gesture spotting with body-worn inertial sensors to detect user activities
Pattern Recognition
Analyzing the kinematics of bivariate pointing
GI '08 Proceedings of graphics interface 2008
Semantic Segmentation of Motion Capture Using Laban Movement Analysis
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Grounding Concrete Motion Concepts with a Linguistic Framework
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Motion segmentation for time-varying mesh sequences based on spherical registration
EURASIP Journal on Applied Signal Processing - 3DTV: Capture, Transmission, and Display of 3D Video
Real time trajectory based hand gesture recognition
WSEAS Transactions on Information Science and Applications
Wearable motion capture suit with full-body tactile sensors
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A group of novel approaches and a toolkit for motion capture data reusing
Multimedia Tools and Applications
Unsupervised clustering in multimodal multiparty meeting analysis
Multimodal corpora
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Finding temporal patterns by data decomposition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Automated gesture segmentation from dance sequences
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Trajectory based hand gesture recognition
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Human activity language: grounding concepts with a linguistic framework
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Human action segmentation and classification based on the Isomap algorithm
Multimedia Tools and Applications
Warped K-Means: An algorithm to cluster sequentially-distributed data
Information Sciences: an International Journal
Rule-based trajectory segmentation for modeling hand motion trajectory
Pattern Recognition
3D human motion analysis framework for shape similarity and retrieval
Image and Vision Computing
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Recognition of human gestures is important for analysis and indexing of video. To recognize human gestures on video, generally a large number of training examples for each individual gesture must be collected. This is a labor-intensive and error-prone process and is only feasible for a limited set of gestures. In this paper, we present an approach for automatically segmenting sequences of natural activities into atomic sections and clustering them. Our work is inspired by natural language processing where words are extracted from long sentences. We extract primitive gestures from sequences of human motion. Our approach contains two steps. First, the sequences of human motion are segmented into atomic components and clustered using a Hidden Markov Model. Thus we can represent the original sequences by discrete symbols. Then we extract lexicon from these discrete sequences by using an algorithm named COMPRESSIVE. Experimental results on music conducting gestures demonstrate the effectiveness of our approach.