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Oriol Rodriguez-Leor; E. Fernandez-Nofrerias; J. Mauri; C. Garcia; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva |
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Intravascular ultrasound segmentation using local binary patterns |
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2003 |
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European Heart Journal (IF: 5.997), ESC Congress 2003 |
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Vienna (Austria) |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ RFM2003a |
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407 |
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O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; A. Tovar; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva |
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Analisis de texturas mediante la modificacion de un modelo binario local para la segmentacion automatica de secuencias de ecografia intracoronaria |
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2003 |
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Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares |
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Sevilla (Spain) |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ RMF2003f |
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413 |
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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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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PuR2003 |
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432 |
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M. Gomez; J. Mauri; E. Fernandez-Nofrerias; Oriol Rodriguez-Leor; Carme Julia; Oriol Pujol; Petia Radeva |
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Diferenciacion de las estructuras del vaso coronario mediante el procesamiento de imagenes y el analisis de las diferentes texturas a partir de la ecografia intracoronaria |
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2002 |
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XXXVIII Congreso Nacional de la Sociedad Española de Cardiologia |
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Madrid |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ GMF2002f |
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433 |
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Oriol Pujol; Petia Radeva |
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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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