Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Object Localisation in Images
International Journal of Computer Vision
Probabilistic Tracking with Exemplars in a Metric Space
International Journal of Computer Vision - Marr Prize Special Issue
Statistical Foreground Modelling for Object Localisation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Proceedings of the 24th DAGM Symposium on Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Mosaicing with Super-Resolution Zoom
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Multimedia Tools and Applications
Affine and projective active contour models
Pattern Recognition
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Multi-scale feature density approximation for object representation and tracking
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A robust template tracking algorithm with weighted active drift correction
Pattern Recognition Letters
Optimal Image and Video Closure by Superpixel Grouping
International Journal of Computer Vision
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The paper describes an approach to the tracking of complex shapes through image sequences, that combines deformable region models and deformable contours. A deformable region model is presented: its optimisation is based on texture correlation and is constrained by the use of a motion model, such as rigid, affine or homographic. The use of texture information (versus edge information) noticeably improves the tracking performances of deformable models in the presence of texture. Then the region contour is refined using an edge based deformable model in order to better deal with specularities, non planar objects and occlusions. The method is illustrated and validated by experimental results on real images.