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Ernest Valveny; Philippe Dosch |
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Title |
Symbol Recognition Contest: A Synthesis |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:368–386, ISBN: 3–540–22478–5 |
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DAG @ dag @ VaD2004b |
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501 |
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Author |
Oriol Ramos Terrades; Ernest Valveny |
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Title |
Indexing Technical Symbols Using Ridgelets Transform |
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Miscellaneous |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:177–187, ISBN: 3–540–22478–5 |
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DAG @ dag @ VaD2004c |
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503 |
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David Masip; Jordi Vitria |
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Title |
Boosted Linear Projections for Discriminant Analysis |
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2004 |
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CCIA 2004, 45–52, ISBN: 1–58603–466–9 |
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IOS Press |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV2004c |
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510 |
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Author |
Josep Llados; Young-Bin Kwon |
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Graphics Recognition. Recent Advances and Perspectives |
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Miscellaneous |
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2004 |
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LNCS 3080, ISBN: 3–540–22478–5 |
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DAG @ dag @ LlK2004 |
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515 |
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Author |
Carme Julia |
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Title |
Motion segmentation through factorization. Application to night driving assistance |
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2004 |
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CVC Technical Report #79 |
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Admin @ si @ 38169 |
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506 |
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Author |
Raul Chaves |
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Title |
Sistema de identificacion mediante huellas dactilares |
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2004 |
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CVC Technical Report #81 |
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Admin @ si @ Cha2004 |
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507 |
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Author |
Oriol Martinez |
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Title |
Semantic Retrieval of Memory Color Content |
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2004 |
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CVC Technical Report #80 |
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Admin @ si @ 38047 |
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508 |
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Jaume Amores; Petia Radeva |
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Registration and retrieval of medical images. Application to IVUS |
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2004 |
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CVC Technical Report #77 |
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ADAS @ adas @ AmR2004 |
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513 |
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Author |
Jaume Garcia |
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Title |
Generalized Active Shape Models Applied to Cardiac Function Analysis |
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2004 |
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CVC Technical Report |
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78 |
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Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography. |
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Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown. |
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CVC (UAB) |
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Master's thesis |
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IAM; |
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IAM @ iam @ Gar2004 |
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1513 |
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