Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
A model for integrated qualitative spatial and dynamic reasoning about physical systems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Qualitative representation of positional information
Artificial Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Agent-Based Simulation of Animal Behaviour
Applied Intelligence
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A System for Tracking Laboratory Animals Based on Optical Flow and Active Contours
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
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The paper presents the LABAQM system developed for the analysis of laboratory animal behaviours. It is based on qualitative modelling of animal motions. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The behaviour learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. We have shown that the qualitative model of behaviour can be modelled by hidden Markov models. The fusion of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours, which are used in the behaviour analysis phase.