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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2008). Morphology Based Handwritten Line Segmentation using Foreground and Background Information. In International Conference on Frontiers in Handwriting Recognition, (241–246).
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Alfons Juan-Ciscar, & Gemma Sanchez. (2008). PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008.
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Marçal Rusiñol, & Josep Llados. (2008). Word and Symbol Spotting using Spatial Organization of Local Descriptors. In Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, (489–496).
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Mathieu Nicolas Delalandre, Ernest Valveny, & Josep Llados. (2008). Performance Evaluation of Symbol Recognition and Spotting Systems. In Proceedings of the 8th International Workshop on Document Analysis Systems, (497–505).
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Joan Mas, Jose Antonio Rodriguez, Dimosthenis Karatzas, Gemma Sanchez, & Josep Llados. (2008). HistoSketch: A Semi-Automatic Annotation Tool for Archival Documents. In Proceedings of the 8th International Workshop on Document Analysis Systems, (517–524).
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Dimosthenis Karatzas. (2008). Detecting Gradients in Text Images Using the Hough Transform. In Proceedings of the 8th International Workshop on Document Analysis Systems, (245–252).
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Ernest Valveny, & Miquel Ferrer. (2008). Application of Graph Embedding to Solve Graph Matchin Problems. In Colloque International Francophone sur l’Ecrit et le Document (13–18).
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Miquel Ferrer, Ernest Valveny, F. Serratosa, K. Riesen, & Horst Bunke. (2008). An Approximate Algorith for Median Graph Computation using Graph Embedding. In 19th International Conference on Pattern Recognition..
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Dimosthenis Karatzas, Marçal Rusiñol, Coen Antens, & Miquel Ferrer. (2008). Segmentation Robust to the Vignette Effect for Machine Vision Systems. In 19th International Conference on Pattern Recognition.
Abstract: The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect.
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Partha Pratim Roy, Umapada Pal, Josep Llados, & F. Kimura. (2008). Convex Hull based Approach for Multi-oriented Character Recognition form Graphical Documents. In 19th International Conference on Pattern Recognition.
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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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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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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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