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Author |
Fernando Vilariño |
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Title |
Unveiling the Social Impact of AI |
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Conference Article |
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2020 |
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Workshop at Digital Living Lab Days Conference |
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September 2020 |
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MV; DAG; 600.121; 600.140;SIAI |
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Admin @ si @ Vil2020 |
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3459 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas |
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Title |
A Living Lab approach for Citizen Science in Libraries |
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Conference Article |
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2016 |
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1st International ECSA Conference |
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Berlin; Germany; May 2016 |
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ECSA |
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MV; DAG; 600.084; 600.097;SIAI |
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Admin @ si @ViK2016 |
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2804 |
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Author |
Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño |
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Title |
Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos |
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Conference Article |
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2014 |
Publication |
CARE workshop |
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Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection |
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We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels. |
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Boston; USA; September 2014 |
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CARE |
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MV; DAG; 600.060; 600.047; 600.077;SIAI |
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Admin @ si @ NBF2014 |
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2504 |
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Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva |
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Title |
Circular Blurred Shape Model for Symbol Spotting in Documents |
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Conference Article |
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Year |
2009 |
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16th IEEE International Conference on Image Processing |
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1985-1988 |
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Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors. |
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Cairo, Egypt |
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978-1-4244-5653-6 |
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ICIP |
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MILAB;HuPBA;DAG |
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no |
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BCNPCL @ bcnpcl @ EFP2009b |
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1184 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva |
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Title |
Multi-class Binary Symbol Classification with Circular Blurred Shape Models |
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Conference Article |
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Year |
2009 |
Publication |
15th International Conference on Image Analysis and Processing |
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5716 |
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1005–1014 |
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Abstract |
Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. |
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Salerno, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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MILAB;HuPBA;DAG |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EFP2009c |
Serial |
1186 |
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Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Gemma Sanchez; Petia Radeva; Oriol Pujol |
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Title |
Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction |
Type |
Book Chapter |
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Year |
2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:13–21 |
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Girona (Spain) |
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MILAB;DAG;HUPBA |
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BCNPCL @ bcnpcl @ FEL2007a |
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775 |
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Author |
Ricardo Toledo; Ramon Baldrich; Ernest Valveny; Petia Radeva |
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Title |
Enhancing snakes for vessel detection in angiography images. |
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Miscellaneous |
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2002 |
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Proceedings of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 139–144. |
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MILAB;DAG;CIC;ADAS |
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BCNPCL @ bcnpcl @ TBV2002 |
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300 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
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Title |
Multi-class Binary Object Categorization using Blurred Shape Models |
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Conference Article |
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2007 |
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Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern |
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4756 |
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773–782 |
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978-3-540-76724-4 |
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CIARP |
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MILAB; DAG;HuPBA |
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BCNPCL @ bcnpcl @ EFP2007 |
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911 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
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Title |
Circular Blurred Shape Model for Multiclass Symbol Recognition |
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Journal Article |
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2011 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
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TSMCB |
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41 |
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2 |
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497-506 |
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In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. |
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1083-4419 |
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MILAB; DAG;HuPBA |
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Admin @ si @ EFP2011 |
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1784 |
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Author |
Debora Gil; Jordi Gonzalez; Gemma Sanchez (eds) |
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Title |
Computer Vision: Advances in Research and Development |
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Year |
2007 |
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Proceedings of the 2nd CVC International Workshop |
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UAB |
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Bellaterra (Spain) |
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Debora Gil; Jordi Gonzalez; Gemma Sanchez |
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978-84-935251-4-9 |
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IAM; ISE; DAG |
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IAM @ iam @ GGS2007 |
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1493 |
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