2003 |
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Josep Llados, Ernest Valveny, Gemma Sanchez, & Enric Marti. (2003). A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa In Proceedings of Pattern Recognition in Information Systems (pp. 1–13). ICEIS Press.
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2002 |
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Ernest Valveny, Ricardo Toledo, Ramon Baldrich, & Enric Marti. (2002)." Combining recognition-based in segmentation-based approaches for graphic symol recognition using deformable template matching" In Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002 (502–507).
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2001 |
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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.
Cite Key: VMA2001
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2000 |
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Ernest Valveny, & Enric Marti. (2000). "Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework " In Proc. 15th Int Pattern Recognition Conf (Vol. 2, pp. 239–242).
Abstract: Hand-drawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in hand-drawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm.
Cite Key: VAM2000
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Josep Llados, Jaime Lopez-Krahe, Gemma Sanchez, & Enric Marti. (2000)." Interprétation de cartes et plans par mise en correspondance de graphes de attributs" In 12 Congrès Francophone AFRIF–AFIA (Vol. 3, pp. 225–234).
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1999 |
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Ernest Valveny, & Enric Marti. (1999). "Application of deformable template matching to symbol recognition in hand-written architectural draw " In Proceedings of the Fifth International Conference on. Bangalore (India).
Abstract: We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified.
Cite Key: GRV2003
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Ernest Valveny, & Enric Marti. (1999)." Recognition of lineal symbols in hand-written drawings using deformable template matching" In Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes.
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Josep Llados, Enric Marti, & Jaime Lopez-Krahe. (1999). "A Hough-based method for hatched pattern detection in maps and diagrams " In Proceeding of the Fifth Int. Conf. Document Analysis and Recognition ICDAR ’99 (pp. 479–482).
Abstract: A hatched area is characterized by a set of parallel straight lines placed at regular intervals. In this paper, a Hough-based schema is introduced to recognize hatched areas in technical documents from attributed graph structures representing the document once it has been vectorized. Defining a Hough-based transform from a graph instead of the raster image allows to drastically reduce the processing time and, second, to obtain more reliable results because straight lines have already been detected in the vectorization step. A second advantage of the proposed method is that no assumptions must be made a priori about the slope and frequency of hatching patterns, but they are computed in run time for each hatched area.
Cite Key: LIM1999b
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1997 |
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Ernest Valveny, & Enric Marti. (1997)." Dimensions analysis in hand-drawn architectural drawings" In VII National Simposium of Pattern Recognition and image Analysis, SNRFAI´97 (pp. 90–91). CVC-UAB.
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Gemma Sanchez, Josep Llados, & Enric Marti. (1997). "A string-based method to recognize symbols and structural textures in architectural plans " In 2nd IAPR Workshop on Graphics Recognition.
Abstract: This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
Cite Key: SLE1997
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