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Author  |
Joan Mas; Jose Antonio Rodriguez; Dimosthenis Karatzas; Gemma Sanchez; Josep Llados |
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Title |
HistoSketch: A Semi-Automatic Annotation Tool for Archival Documents |
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Conference Article |
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2008 |
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Proceedings of the 8th International Workshop on Document Analysis Systems, |
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517–524 |
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Nara (Japan) |
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DAG @ dag @ MRK2008a |
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1061 |
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Author |
Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge |
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Title |
A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams |
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2010 |
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Pattern Recognition |
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PR |
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43 |
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12 |
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4148–4164 |
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Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing |
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This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings. |
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DAG @ dag @ MLS2010 |
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1336 |
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Joan Oliver; Ricardo Toledo; J. Pujol; J. Sorribes; E. Valderrama |
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Title |
Un ABP basado en la robotica para las ingenierias informaticas |
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2009 |
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15th Jornadas de Enseñanza Universitaria de la Informatica |
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331–338 |
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Barcelona, Spain |
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ISBN:978–84–692–2758–9 |
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JENUI |
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Admin @ si @ OTP2009 |
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1253 |
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Joan Serrat |
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Aplicacion del analisis de imagenes en radiologia. |
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1995 |
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To be published in a CD––ROM edited by AERFAI, the Spanish image Analysis and Pattern. |
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ADAS @ adas @ Ser1995 |
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120 |
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Joan Serrat; Antonio Lopez |
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Una experiencia de Enginyeria del Software amb ABP |
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2006 |
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III Jornada de Campus d’Innovacio Docent |
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Bellaterra (Spain) |
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ADAS @ adas @ SeL2006 |
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941 |
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Joan Serrat; Antonio Lopez |
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Title |
Deteccion automatica de lineas de carril para la asistencia a la conduccion |
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Miscellaneous |
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2010 |
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UAB Divulga – Revista de divulgacion cientifica |
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La detección por cámara de las líneas de carril en las carreteras puede ser una solución asequible a los riesgos de conducción generados por los adelantamientos o las salidas de carril. Este trabajo propone un sistema que funciona en tiempo real y que obtiene muy buenos resultados. El sistema está preparado para identificar las líneas en condiciones de visibilidad poco favorables, como puede ser la conducción nocturna o con otros vehículos que dificulten la visión. |
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Bellaterra (Spain) |
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ADAS @ adas @ SeL2010 |
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1430 |
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Joan Serrat; Antonio Lopez; David Lloret |
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On ridges and valleys. |
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2000 |
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15 th International Conference on Pattern Recognition |
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4 |
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59-66 |
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Barcelona |
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ICPR |
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ADAS @ adas @ SLL2000 d |
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334 |
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Joan Serrat; Enric Marti |
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Elastic matching using interpolation splines |
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1991 |
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IV Spanish Symposium of Pattern Recognition and image Analysis |
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ADAS;IAM; |
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IAM @ iam @ SMV1991 |
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1651 |
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Joan Serrat; Felipe Lumbreras; Antonio Lopez |
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Title |
Cost estimation of custom hoses from STL files and CAD drawings |
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2013 |
Publication |
Computers in Industry |
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COMPUTIND |
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64 |
Issue |
3 |
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299-309 |
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On-line quotation; STL format; Regression; Gaussian process |
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We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%. |
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ADAS; 600.057; 600.054; 605.203 |
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Admin @ si @ SLL2013; ADAS @ adas @ |
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2161 |
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Author |
Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas |
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Title |
myStone: A system for automatic kidney stone classification |
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2017 |
Publication |
Expert Systems with Applications |
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ESA |
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89 |
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41-51 |
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Kidney stone; Optical device; Computer vision; Image classification |
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Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. |
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ADAS; MSIAU; 603.046; 600.122; 600.118 |
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Admin @ si @ SLB2017 |
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3026 |
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Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
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Learning to measure for preshipment garment sizing |
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2018 |
Publication |
Measurement |
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MEASURE |
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130 |
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327-339 |
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Apparel; Computer vision; Structured prediction; Regression |
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Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
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ADAS; MSIAU; 600.122; 600.118 |
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Admin @ si @ SLR2018 |
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3128 |
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Joan Serrat; Ferran Diego; Felipe Lumbreras |
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Los faros delanteros a traves del objetivo |
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2008 |
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UAB Divulga, Revista de divulgacion cientifica |
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ADAS @ adas @ SDL2008b |
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1471 |
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Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez |
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Synchronization of Video Sequences from Free-moving Cameras |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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620–627 |
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Girona (Spain) |
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J. Marti et al. |
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IbPRIA |
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ADAS @ adas @ SDL2007 |
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880 |
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Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira |
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Dynamic Comparison of Headlights |
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2008 |
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Journal of Automobile Engineering |
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222 |
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5 |
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643–656 |
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video alignment |
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ADAS @ adas @ SDL2008a |
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958 |
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Joan Serrat; Ferran Diego; Jose Manuel Alvarez; Felipe Lumbreras |
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Alignment of Videos Recorded from Moving Vehicles |
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2007 |
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in 14th International Conference on Image Analysis and Processing, |
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512–517 |
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Modena (Italia) |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ SDA2007 |
Serial |
879 |
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| Permanent link to this record |
