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Author |
F. Pla; Petia Radeva; Jordi Vitria |
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Non-parametric distance-based classification techniques and their applications |
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2008 |
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Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications |
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11 |
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3-4 |
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223–225 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ PRV2008 |
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999 |
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Author |
E. Provenzi; Carlo Gatta; M. Fierro; A. Rizzi |
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A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Constant |
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2008 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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30 |
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10 |
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1757–1770 |
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MILAB |
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BCNPCL @ bcnpcl @ PGF2008 |
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1001 |
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Oriol Rodriguez-Leor; Carlo Gatta; E. Fernandez-Nofrerias; Oriol Pujol; Neus Salvatella; C. Bosch; H. Tizon; Petia Radeva; J. Mauri |
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Computationally Efficient Image-based IVUS Pullbacks Gating |
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2008 |
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European Heart Journal, ESC Supplement, Munich, 2008, p. 775 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ RGF2008 |
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1036 |
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Carolina Malagelada; Fosca De Iorio; Fernando Azpiroz; Anna Accarino; Santiago Segui; Petia Radeva; Juan R. Malagelada |
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Title |
New Insight Into Intestinal Motor Function via Noninvasive Endoluminal Image Analysis |
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2008 |
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Gastroenterology |
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135 |
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4 |
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1155–1162 |
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MILAB |
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BCNPCL @ bcnpcl @ MDA2008 |
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1040 |
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Author |
Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva |
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Title |
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification |
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2009 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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10 |
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1 |
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113–126 |
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Abstract |
The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. |
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1524-9050 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ BEV2008 |
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1116 |
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