Tracking and data association
Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Discriminant Snakes for 3D Reconstruction in Medical Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
A class of constrained clustering algorithms for object boundary extraction
IEEE Transactions on Image Processing
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Algorithms for tracking generic 2D object boundaries in a video sequence should not make strong assumptions about the shapes to be tracked. When only a weak prior is at hand, the tracker performance becomes heavily dependent on its ability to detect image features; to classify them as informative (i.e., belonging to the object boundary) or as outliers; and to match the informative features with corresponding model points. Unlike simpler approaches often adopted in tracking problems, this work looks at feature classification and matching as two unsupervised learning problems. Consequently, object tracking is converted into a problem of dynamic clustering of data, which is solved using competitive learning algorithms. It is shown that competitive learning is a key technique for obtaining accurate local motion estimates (avoiding aperture problems) and for discarding the outliers. In fact, the competitive learning approach shows several benefits: (i) a gradual propagation of shape information across the model; (ii) the use of shape and noise models competing for explaining the data; and (iii) the possibility of adopting high dimensional feature spaces containing relevant information extracted from the image. This work extends the unified framework proposed by the authors in [1].