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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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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 |
Misael Rosales; Petia Radeva; J. Mauri; Oriol Pujol |
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Title |
Simulation Model of Intravascular Ultrasound Images |
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2004 |
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MICCAI, 2004, Saint Melo, France |
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Springer Verlag |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ RRM2004b |
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464 |
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Author |
Georg Langs; Petia Radeva; David Rotger; Francesc Carreras |
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Title |
Building and Registering Parameterized 3D Models of Vessel Trees for Visualization during Intervention |
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2004 |
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17th International Conference on Pattern Recognition, ICPR’04 |
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Cambridge, United Kingdom |
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MILAB |
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BCNPCL @ bcnpcl @ LRR2004b |
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463 |
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Author |
Sergio Escalera; Petia Radeva |
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Title |
Fast greyscale road sign model matching and recognition |
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2004 |
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CCIA, IOS Press |
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Barcelona, Spain |
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HuPBA; MILAB |
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BCNPCL @ bcnpcl @ EsR2004 |
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469 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva; Theo Gevers; A. Smeulders |
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Title |
Boosting Contextual Information in Content-based Image Retrieval |
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Miscellaneous |
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2004 |
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6th ACM SIGMM International Workshop on Multimedia Information Retrieval |
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New York, USA |
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MILAB;ISE |
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ADAS @ adas @ ASR2004 |
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466 |
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Author |
Fernando Vilariño; Debora Gil; Petia Radeva |
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Title |
A Novel FLDA Formulation for Numerical Stability Analysis |
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2004 |
Publication |
Recent Advances in Artificial Intelligence Research and Development |
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113 |
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77-84 |
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Supervised Learning; Linear Discriminant Analysis; Numerical Stability; Computer Vision |
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Abstract |
Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision. |
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IOS Press |
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J. Vitrià, P. Radeva and I. Aguiló |
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978-1-58603-466-5 |
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MV;IAM;MILAB;SIAI |
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IAM @ iam @ VGR2004 |
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1663 |
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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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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 |
Publication |
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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IAM;MILAB |
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BCNPCL @ bcnpcl @ GiR2004b |
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491 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva; E.N.Nofrerias |
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Title |
Anisotropic processing of image structures for adventitia detection in intravascular ultrasound images |
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2004 |
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Proc. Computers in Cardiology |
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31 |
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229-232 |
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The adventitia layer appears as a weak edge in IVUS images with a non-uniform grey level, which difficulties its detection. In order to enhance edges, we apply an anisotropic filter that homogenizes the grey level along the image significant structures (ridges, valleys and edges). A standard edge detector applied to the filtered image yields a set of candidate points prone to be unconnected. The final model is obtained by interpolating the former line segments along the tangent direction to the level curves of the filtered image with an anisotropic contour closing technique based on functional extension principles |
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Chicago (USA) |
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IAM; MILAB |
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IAM @ iam @ HGR2004 |
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1555 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Shape Restoration via a Regularized Curvature Flow |
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2004 |
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Journal of Mathematical Imaging and Vision |
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21 |
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3 |
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205-223 |
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Any image filtering operator designed for automatic shape restoration should satisfy robustness (whatever the nature and degree of noise is) as well as non-trivial smooth asymptotic behavior. Moreover, a stopping criterion should be determined by characteristics of the evolved image rather than dependent on the number of iterations. Among the several PDE based techniques, curvature flows appear to be highly reliable for strongly noisy images compared to image diffusion processes.
In the present paper, we introduce a regularized curvature flow (RCF) that admits non-trivial steady states. It is based on a measure of the local curve smoothness that takes into account regularity of the curve curvature and serves as stopping term in the mean curvature flow. We prove that this measure decreases over the orbits of RCF, which endows the method with a natural stop criterion in terms of the magnitude of this measure. Further, in its discrete version it produces steady states consisting of piece-wise regular curves. Numerical experiments made on synthetic shapes corrupted with different kinds of noise show the abilities and limitations of each of the current geometric flows and the benefits of RCF. Finally, we present results on real images that illustrate the usefulness of the present approach in practical applications. |
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IAM;MILAB |
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IAM @ iam @ GiR2004c |
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1532 |
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Author |
Jordi Gonzalez |
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Title |
Human Sequence Evaluation: the Key-frame Approach |
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2004 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Xavier Roca;Javier Varona |
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ISE @ ise @ Gon2004 |
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362 |
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Author |
Robert Benavente; Maria Vanrell |
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Title |
Fuzzy Colour Naming Based on Sigmoid Membership Functions. |
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2004 |
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CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 |
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CIC |
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CAT @ cat @ BeV2004 |
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441 |
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M. Bressan; David Guillamet; Jordi Vitria |
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Multiclass Object Recognition using Class-Conditional Independent Component Analisis |
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2004 |
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Cybernetics and Systems, 35/1:35–61 (IF: 0.768) |
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OR;MV |
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BCNPCL @ bcnpcl @ BGV2004 |
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442 |
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David Guillamet |
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Statistical Local Appearance Models for Object Recognition |
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2004 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Bellaterra |
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Ph.D. thesis |
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Jordi Vitria |
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Admin @ si @ Gui2004 |
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444 |
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