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Jose Manuel Alvarez, & Antonio Lopez. (2008). Novel Index for Objective Evaluation of Road Detection Algorithms. In Intelligent Transportation Systems. 11th International IEEE Conference on, (815–820).
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Miquel Ferrer, Ernest Valveny, F. Serratosa, & Horst Bunke. (2008). Exact Median Graph Computation via Graph Embedding. In 12th International Workshop on Structural and Syntactic Pattern Recognition (Vol. 5324, 15–24). LNCS.
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Jose Antonio Rodriguez, Florent Perronnin, Gemma Sanchez, & Josep Llados. (2008). Unsupervised writer style adaptation for handwritten word spotting. In Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award..
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Alicia Fornes, Josep Llados, Gemma Sanchez, & Horst Bunke. (2008). Writer Identification in Old Handwritten Music Scores. In Proceedings of the 8th International Workshop on Document Analysis Systems, (347–353).
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Ferran Diego, Daniel Ponsa, Joan Serrat, & Antonio Lopez. (2008). Video Alignment for Difference-spotting.
Keywords: video alignment
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2008). Recognition of Multi-oriented Touching Characters in Graphical Documents. In Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on, (Vol. 16, 297–304).
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Enric Marti, Ferran Poveda, Antoni Gurgui, Jaume Rocarias, Debora Gil, & Aura Hernandez-Sabate. (2013). Una experiencia de estructura, funcionamiento y evaluación de la asignatura de graficos por computador con metodologia de aprendizaje basado en proyectos.
Abstract: IV Congreso Internacional UNIVEST
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Josep Llados, & Marçal Rusiñol. (2014). Graphics Recognition Techniques. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 489–521). Springer London.
Abstract: This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process.
Keywords: Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation
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Muhammad Muzzamil Luqman, Jean-Yves Ramel, & Josep Llados. (2012). Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 243–253). LNCS. Springer Berlin Heidelberg.
Abstract: Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.
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Muhammad Muzzamil Luqman, Thierry Brouard, Jean-Yves Ramel, & Josep Llados. (2012). Recherche de sous-graphes par encapsulation floue des cliques d'ordre 2: Application à la localisation de contenu dans les images de documents graphiques. In Colloque International Francophone sur l'Écrit et le Document (pp. 149–162).
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Antonio Lopez, J. Hilgenstock, A. Busse, Ramon Baldrich, Felipe Lumbreras, & Joan Serrat. (2008). Nightime Vehicle Detecion for Intelligent Headlight Control. In Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, (Vol. 5259, 113–124). LNCS.
Keywords: Intelligent Headlights; vehicle detection
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Jose Antonio Rodriguez, Gemma Sanchez, & Josep Llados. (2008). Categorization of Digital Ink Elements using Spectral Features. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 188–198). LNCS. Springer–Verlag.
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David Aldavert, & Ricardo Toledo. (2008). Stereo Vision Local Map Alignment for Robot Environment Mapping. In Robot Vision Second International Workshop, RobVis (Vol. 4931, 111–124). LNCS.
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Enric Marti, Jaume Rocarias, Debora Gil, Marc Vivet, & Carme Julia. (2008). Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Graficos por Computador.
Abstract: En esta comunicación presentamos una experiencia en Aprendizaje Basado en Proyectos (Project
Based Learning – PBL) realizada los últimos cuatro años (cursos del 2004-05 al 2007-08) en Gráficos
por Computador 2, asignatura optativa de tercer curso de Ingeniería Informática, titulación impartida
en la Escuela Técnica Superior de Ingeniería (ETSE) de la Universidad Autónoma de Barcelona
(UAB).
Fruto de la constante voluntad de mejora de la organización ABP de nuestra asignatura nos decidimos
a utilizar una herramienta LMS (Learning Management System) basada en Moodle y adaptada por
nosotros llamada Caronte para poder gestionar la documentación generada en ABP, y añadir una
componente semipresencial a la asignatura.
En primer lugar se presenta la organización de nuestra asignatura, basada proponer al alumno dos
itinerarios para cursarla: el itinerario ABP y el itinerario basado en clases magistrales i examen que
llamaremos TPPE (Teoría, Problemas, Prácticas, Examen). La dinámica ABP nos genera una cantidad
importante de documentación entre los grupos y el profesor, aparte de el feedback que el profesor
genera a los alumnos.
En la segunda parte del artículo presentamos los espacios docentes electrónicos de ambos itinerarios,
con los que trabajan los alumnos.
Finalmente, mostramos los resultados obtenidos de alumnos matriculados y de encuestas de valoración
realizados por los alumnos para finalmente exponer las conclusiones de estos cuatro años de
experiencia en ABP y en el uso de recursos virtuales en ABP, así como plantear mejoras y temas de
discusión sobre ABP.
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Dani Rowe. (2008). Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments.
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