Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Incremental Focus of Attention for Robust Vision-Based Tracking
International Journal of Computer Vision
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
Digital Image Processing
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)
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
Fast Temporal Dynamics of Visual Cue Integration
Fast Temporal Dynamics of Visual Cue Integration
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Probabilistic and Voting Approaches to Cue Integration for Figure-Ground Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Tracking regions of human skin through illumination changes
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Ontological inference for image and video analysis
Machine Vision and Applications
Multi-sensory and Multi-modal Fusion for Sentient Computing
International Journal of Computer Vision
Pre-Attentive and Attentive Detection of Humans in Wide-Field Scenes
International Journal of Computer Vision
Adaptive probabilistic tracking embedded in smart cameras for distributed surveillance in a 3D model
EURASIP Journal on Embedded Systems
Vision-Based Detection of Mobile Smart Objects
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
Vision-based production of personalized video
Image Communication
Colour, texture, and motion in level set based segmentation and tracking
Image and Vision Computing
A self-referential perceptual inference framework for video interpretation
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Multi-object tracking based on a modular knowledge hierarchy
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Self-organizing computer vision for robust object tracking in smart cameras
ATC'10 Proceedings of the 7th international conference on Autonomic and trusted computing
A hierarchical feature fusion framework for adaptive visual tracking
Image and Vision Computing
A robust particle filter-based face tracker using combination of color and geometric information
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
A large margin framework for single camera offline tracking with hybrid cues
Computer Vision and Image Understanding
Disagreement-Based multi-system tracking
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Cooperative augmentation of mobile smart objects with projected displays
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on interaction with smart objects, Special section on eye gaze and conversation
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Even though many of today's vision algorithms are very successful, they lack robustness since they are typically limited to a particular situation. In this paper we argue that the principles of sensor and model integration can increase the robustness of today's computer vision systems substantially. As an example multi-cue tracking of faces is discussed. The approach is based on the principles of self-organization of the integration mechanism and self-adaptation of the cue models during tracking. Experiments show that the robustness of simple models is leveraged significantly by sensor and model integration.