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Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Ole Larsen; Petia Radeva; Enric Marti |
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Title |
Bounds on the optimal elasticity parameters for a snake |
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1995 |
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Image Analysis and Processing |
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37-42 |
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This paper develops a formalism by which an estimate for the upper and lower bounds for the elasticity parameters for a snake can be obtained. Objects different in size and shape give rise to different bounds. The bounds can be obtained based on an analysis of the shape of the object of interest. Experiments on synthetic images show a good correlation between the estimated behaviour of the snake and the one actually observed. Experiments on real X-ray images show that the parameters for optimal segmentation lie within the estimated bounds. |
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MILAB;IAM |
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IAM @ iam @ LRM1995a |
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1559 |
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Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Oriol Pujol; Debora Gil; Petia Radeva |
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Title |
Fundamentals of Stop and Go active models |
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Journal Article |
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2005 |
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Image and Vision Computing |
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23 |
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8 |
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681-691 |
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Deformable models; Geodesic snakes; Region-based segmentation |
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An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation. |
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Butterworth-Heinemann |
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Newton, MA, USA |
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0262-8856 |
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IAM;MILAB;HuPBA |
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IAM @ iam @ PGR2005 |
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1629 |
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Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Oriol Pujol; Petia Radeva |
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Texture Segmentation by Statistic Deformable Models |
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2003 |
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International Journal of Image and Graphics (IJIG) |
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BCNPCL @ bcnpcl @ PuR2003 |
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432 |
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Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Oriol Pujol; Petia Radeva |
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Title |
Texture Segmentation by Statistical Deformable Models |
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2004 |
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International Journal of Image and Graphics |
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IJIG |
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4 |
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3 |
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433-452 |
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Texture segmentation, parametric active contours, statistic snakes |
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Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model. |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PuR2004a |
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505 |
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Author ![sorted by Author field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Oriol Pujol; Petia Radeva; Jordi Vitria |
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Title |
Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes |
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2006 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ PRV2006a |
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646 |
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