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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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Bogdan Raducanu; Jordi Vitria |
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Face Recognition by Artificial Vision Systems: A Cognitive Perspective |
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2008 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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22 |
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5 |
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899–913 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008b |
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1007 |
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Author |
Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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Title |
Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images |
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2014 |
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IEEE Transactions on Information Technology in Biomedicine |
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TITB |
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18 |
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6 |
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1831-1838 |
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Wireless Capsule Endoscopy; Small Bowel Motility Dysfunction; Contraction Detection; Structured Prediction; Betweenness Centrality |
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Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the folding of the intestinal wall. In this paper we present a new method to robustly detect wrinkle frames in full WCE videos by using a new mid-level image descriptor that is based on a centrality measure proposed for graphs. We present an extended validation, carried out in a very large database, that shows that the proposed method achieves state of the art performance for this task. |
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OR; MILAB; 600.046;MV |
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Admin @ si @ SDZ2014 |
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2385 |
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Fadi Dornaika; Bogdan Raducanu |
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3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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2008 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2008d |
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1109 |
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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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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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