Using middle level features for robust shape tracking
Pattern Recognition Letters
Normalized Gradient Vector Diffusion and Image Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Competitive Segmentation: A Struggle for Image Space
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Shape Tracking Using Centroid-Based Methods
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
A TASOM-based algorithm for active contour modeling
Pattern Recognition Letters
A New Active Convex Hull Model for Image Regions
Journal of Mathematical Imaging and Vision
On the use of divergence distance in fuzzy clustering
Fuzzy Optimization and Decision Making
An improved time-adaptive self-organizing map for high-speed shape modeling
Pattern Recognition
An efficient approach for building customer profiles from business data
Expert Systems with Applications: An International Journal
Arranging and interpolating sparse unorganized feature points with geodesic circular arc
IEEE Transactions on Image Processing
Geometric attraction-driven flow for image segmentation and boundary detection
Journal of Visual Communication and Image Representation
Coarse-to-fine boundary location with a SOM-like method
IEEE Transactions on Neural Networks
Pattern Recognition Letters
Gradient vector flow based on anisotropic diffusion
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
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Boundary extraction is a key task in many image analysis operations. This paper describes a class of constrained clustering algorithms for object boundary extraction that includes several well-known algorithms proposed in different fields (deformable models, constrained clustering, data ordering, and traveling salesman problems). The algorithms belonging to this class are obtained by the minimization of a cost function with two terms: a quadratic regularization term and an image-dependent term defined by a set of weighting functions. The minimization of the cost function is achieved by lowpass filtering the previous model shape and by attracting the model units toward the centroids of their attraction regions. To define a new algorithm belonging to this class, the user has to specify a regularization matrix and a set of weighting functions that control the attraction of the model units toward the data. The usefulness of this approach is twofold: it provides a unified framework for many existing algorithms in pattern recognition and deformable models, and allows the design of new recursive schemes