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Maya Dimitrova, Petia Radeva, David Rotger, D. Boyadjiev, & Juan J. Villanueva. (2004). Advanced Cardiological Diagnosis via Intelligent Image Analysis.
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Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, E. Fernandez, V. Valle, & Petia Radeva. (2004). Automatic segmentation and characterization of IVUS images by texture analysis.
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O. Rodriguez, David Rotger, J. Mauri, E. Fernandez, V. Valle, & Petia Radeva. (2004). Active vessel workstation: three-dimensional reconstruction of coronary arteries by fusion of angiography and intravascular ultrasound.
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Jian Yang, Zhong Jin, Jing-Yu Yang, David Zhang, & Alejandro F. Frangi. (2004). Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recognition, 37(10): 2097–2100 (IF: 2.176).
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Yong Xu, Jing-Yu Yang, & Zhong Jin. (2004). A novel method for Fisher discriminant analysis. Pattern Recognition, 37(2):381–384 (IF: 2.176).
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Zhong Jin, Franck Davoine, & Zhen Lou. (2004). An Effective EM Algorithm for PCA Mixture Model.
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Antonio Lopez, Felipe Lumbreras, Joan Serrat, & Juan J. Villanueva. (1999). Evaluation of Methods for Ridge and Valley Detection.
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2004). Estimation of Fuzzy Sets for Computational Colour Categorization. Color Research and Application, 29(5):342–353 (IF: 0.739).
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A.F. Sole, Antonio Lopez, & G. Sapiro. (2001). Crease Enhancement Diffusion. Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298), .
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A. Pujol, Antonio Lopez, Jose Luis Alba, & Juan J. Villanueva. (2001). Ridges, Valleys and Hausdorff Based Similarity Measures for Face Detection and Matching.
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David Lloret, Antonio Lopez, & Joan Serrat. (1998). Precise registration of CT and MR volumes based on a new creaseness measure.
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Antonio Lopez. (1997). Ridge/Valley-like structures: Creases, separatrices and drainage patterns.
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Bart M. Ter Haar Romeny, W. Niessen, J. Weickert, P. Van Roermund, W. Van Enk, Antonio Lopez, et al. (1996). Orientation detection of trabecular bone.
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David Lloret, Antonio Lopez, & Joan Serrat. (1997). Rigid Registration of CT and MR volumes based on Rothes creases.
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Debora Gil, & Petia Radeva. (2004). A Regularized Curvature Flow Designed for a Selective Shape Restoration. IEEE Transactions on Image Processing, 13, 1444–1458.
Abstract: 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.
Keywords: Geometric flows, nonlinear filtering, shape recovery.
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