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A. Quingles. (2001). Particio de sòlids.
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E. Sanchez. (2001). On-line recognition of handwritten symbols.
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J.M. Sanchez, & X. Binefa. (2001). Semantics from motion in news videos..
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Joan Serrat, X. Varona, Antonio Lopez, Xavier Roca, & Juan J. Villanueva. (2001). P3: a three-dimensional digitizer prototype..
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X. Varona. (2001). Seguimiento visual robusto en entornos complejos, Tesis..
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Petia Radeva, & Jordi Vitria. (2001). Region-Based Approach for Discriminant Snakes.
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A.F. Sole, Antonio Lopez, & G. Sapiro. (2001). Crease Enhancement Diffusion. Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298), .
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A. Pujol, Antonio Lopez, Jose Luis Alba, & Juan J. Villanueva. (2001). Ridges, Valleys and Hausdorff Based Similarity Measures for Face Detection and Matching.
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Daniel Ponsa. (2001). A model based pedestrian tracking review.
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Josep Llados, Enric Marti, & Juan J.Villanueva. (2001). Symbol recognition by error-tolerant subgraph matching between region adjacency graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1137–1143.
Abstract: The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.
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Ernest Valveny, & Enric Marti. (2001). Learning of structural descriptions of graphic symbols using deformable template matching. In Proc. Sixth Int Document Analysis and Recognition Conf (pp. 455–459).
Abstract: Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structural methods, symbols are usually manually defined from expertise knowledge, and not automatically infered from sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching.
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Lluis Barcelo, & X. Binefa. (2002). Bayesian Video Mosaicing with moving objects. International Journal of Pattern Recognition and Artificial Intelligence, 16(3): 341–348 (IF: 0.359).
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Cristina Cañero, Fernando Vilariño, & Petia Radeva. (2002). Predictive (un) distortion model and 3D Reconstruction by Biplane Snakes. IEEE Transactions on Medical Imaging (IF: 2.911).
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J. Martinez. (2002). Automotive sector and Machine Vision..
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J. Martinez, & F. Thomas. (2002). Efficient Computation of Local Geometric Moments. IEEE Transactions on Image Porcessing, 11(9): 1102–1111 (IF: 2.553).
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