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J. Mauri; E Fernandez-Nofrerias; A. Tovar; E. Martinez; L. Cano; V. Valle; David Rotger; Cristina Cañero; Petia Radeva |
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Ecografia Intracoronaria: Un Nou Pas, la Fusio de Imatges amb la Angiografia, el Software. |
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2001 |
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Revista de la Societat Catalana de Cardiologia, XIIIe Congres de la Societat Catalana de Cardiologia, 4(1):48. |
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BCNPCL @ bcnpcl @ MFT2001 |
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136 |
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Author |
V. Valev; B. Sankur; Petia Radeva |
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Title |
Generalized Non Reducible Descriptors. |
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2000 |
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15 th International Conference on Pattern Recognition, 2: 394–397. |
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Barcelona. |
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BCNPCL @ bcnpcl @ VSR2000 |
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230 |
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Cristina Cañero; Petia Radeva |
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Title |
Vesselness enhancement diffusion |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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16 |
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3141–3151 |
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IF: 0.809 |
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BCNPCL @ bcnpcl @ CaR2003 |
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371 |
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O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; C. Garcia; R. Villuendas; A. Tovar; A. Duran; V. Valle; Misael Rosales; Petia Radeva |
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Title |
Model Empiric de Simulacio d Ecografia Intravascular |
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2003 |
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Revista Societat Catalana de Cardiologia, 4(4):42, XIVe Congres de la Societat Catalana de Cardiologia |
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Barcelona |
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BCNPCL @ bcnpcl @ RMF2003e |
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412 |
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Author |
Debora Gil; Petia Radeva |
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Title |
A Regularized Curvature Flow Designed for a Selective Shape Restoration |
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2004 |
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IEEE Transactions on Image Processing |
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13 |
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1444–1458 |
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Geometric flows, nonlinear filtering, shape recovery. |
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Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization. |
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BCNPCL @ bcnpcl @ GiR2004b |
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491 |
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