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Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
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Title |
Advances in Vision-Based Human Body Modeling |
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2004 |
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3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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1-26 |
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N. Sarris and M. Strintzis. |
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1-59140-299-9 |
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no |
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ADAS @ adas @ SAG2004a |
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458 |
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Author |
Jian Yang; Zhong Jin; Jing-Yu Yang; David Zhang; Alejandro F. Frangi |
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Title |
Essence of kernel Fisher discriminant: KPCA plus LDA |
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2004 |
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Pattern Recognition, 37(10): 2097–2100 (IF: 2.176) |
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Admin @ si @ YJY2004 |
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480 |
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Author |
Yong Xu; Jing-Yu Yang; Zhong Jin |
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Title |
A novel method for Fisher discriminant analysis |
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2004 |
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Pattern Recognition, 37(2):381–384 (IF: 2.176) |
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Admin @ si @ XYJ2004 |
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481 |
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Author |
Antonio Lopez; Felipe Lumbreras; Joan Serrat; Juan J. Villanueva |
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Title |
Evaluation of Methods for Ridge and Valley Detection |
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Miscellaneous |
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1999 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 21:327–335 |
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ADAS @ adas @ LLS1999b |
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483 |
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Author |
Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Title |
Estimation of Fuzzy Sets for Computational Colour Categorization |
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2004 |
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Color Research and Application, 29(5):342–353 (IF: 0.739) |
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CIC |
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CAT @ cat @ BVB2004 |
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484 |
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Bart M. Ter Haar Romeny; W. Niessen; J. Weickert; P. Van Roermund; W. Van Enk; Antonio Lopez; R. Maas |
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Title |
Orientation detection of trabecular bone |
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1996 |
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Biophysics and Molecular Biology, International Biophysics Congress. Volume 65, pgs. P–H5–43 |
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ADAS @ adas @ HNW1996 |
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489 |
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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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Year |
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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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. |
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IAM;MILAB |
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BCNPCL @ bcnpcl @ GiR2004b |
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491 |
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Author |
Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis |
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Title |
Human Motion Tracking and Recognition |
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Book Chapter |
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2005 |
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Encyclopedia of Information Science and Technology, 1(5):1355–1360 |
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ADAS @ adas @ ASG2005 |
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496 |
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Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; N. Grammalidis |
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Title |
Survey of 3D Human Body Representations |
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2005 |
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Encyclopedia of Information Science and Technology, 1(5):2696–2701 |
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ADAS @ adas @ SAM2005a |
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497 |
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Author |
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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