Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Multi-Modal Tracking of Faces for Video Communications
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
LAFTER: Lips and Face Real-Time Tracker
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Self-Organized Integration of Adaptive Visual Cues for Face Tracking
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Towards an active visual observer
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Computational Model of Depth-Based Attention
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Fast Temporal Dynamics of Visual Cue Integration
Fast Temporal Dynamics of Visual Cue Integration
Journal of Cognitive Neuroscience
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Many of today's vision algorithms are very successful in controlled environments. Real-world environments, however, cannot be controlled and are most often dynamic with respect to illumination changes, motion, occlusions, multiple people, etc. Since most computer vision algorithms are limited to a particular situation they lack robustness in the context of dynamically changing environments. In this paper we argue that the integration of information coming from different visual cues and models is essential to increase robustness as well as generality of computer vision algorithms. Two examples are discussed where robustness of simple models is leveraged by cue and model integration. In the first example mutual information is used as a means to combine different object models for face detection without prior learning. The second example discusses experimental results on multi-cue tracking of faces based on the principles of self-organization of the integration mechanism and self-adaptation of the cue models during tracking.