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Josep Llados, Horst Bunke and Enric Marti. 1997. Using Cyclic String Matching to Find Rotational and Reflectional Symmetries in Shapes. Intelligent Robots: Sensing, Modeling and Planning. World Scientific Press, 164–179.
Abstract: Dagstuhl Workshop
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Josep Llados, Horst Bunke and Enric Marti. 1996. Structural Recognition of hand drawn floor plans. VI National Symposium on Pattern Recognition and Image Analysis. Cordoba.
Abstract: A system to recognize hand drawn architectural drawings in a CAD environment has been deve- loped. In this paper we focus on its high level interpretation module. To interpret a floor plan, the system must identify several building elements, whose description is stored in a library of pat- terns, as well as their spatial relationships. We propose a structural approach based on subgraph isomorphism techniques to obtain a high-level interpretation of the document. The vectorized input document and the patterns to be recognized are represented by attributed graphs. Discrete relaxation techniques (AC4 algorithm) have been applied to develop the matching algorithm. The process has been divided in three steps: node labeling, local consistency and global consistency verification. The hand drawn creation causes disturbed line drawings with several accuracy errors, which must be taken into account. Here we have identified them and the AC4 algorithm has been adapted to manage them.
Keywords: Rotational Symmetry; Reflectional Symmetry; String Matching.
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Josep Llados and Enric Marti. 1999. A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems. Machine Graphics & Vision, 8, 195–211.
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Josep Llados and Enric Marti. 1997. Playing with error-tolerant subgraph isomorphism in line drawings. VII National Symposium on Pattern Recognition and image Analysis.
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Josep Llados, Enric Marti and Jordi Regincos. 1993. Interpretación de diseños a mano alzada como técnica de entrada a un sistema CAD en un ámbito de arquitectura. III National Conference on Computer Graphics. Granada.
Abstract: En los últimos años, se ha introducido ámpliamente el uso de los sistemas CAD en dominios relacionados con la arquitectura. Dichos sistemas CAD son muy útiles para el arquitecto en el diseño de planos de plantas de edificios. Sin embargo, la utilización eficiente de un CAD requiere un tiempo de aprendizaje, en especial, en la etapa de creación y edición del diseño. Además, una vez familiarizado con un CAD, el arquitecto debe adaptarse a la simbología que éste le permite que, en algunos casos puede ser poco flexible.Con esta motivación, se propone una técnica alternativa de entrada de documentos en sistemas CAD. Dicha técnica se basa en el diseño del plano sobre papel mediante un dibujo lineal hecho a mano alzada a modo de boceto e introducido mediante scanner. Una vez interpretado este dibujo inicial e introducido en el CAD, el arquitecto sólo deber hacer sobre éste los retoques finales del documento.El sistema de entrada propuesto se compone de dos módulos principales: En primer lugar, la extracción de características (puntos característicos, rectas y arcos) de la imagen obtenida mediante scanner. En dicho módulo se aplican principalmente técnicas de procesamiento de imágenes obteniendo como resultado una representaci¢n del dibujo de entrada basada en grafos de atributos. El objetivo del segundo módulo es el de encontrar y reconocer las entidades integrantes del documento (puertas, mesas, etc.) en base a una biblioteca de símbolos definida en el sistema CAD. La implementación de dicho módulo se basa en técnicas de isomorfismo de grafos.El sistema propone una alternativa que permita, mediante el diseño a mano alzada, la introducción de la informaci¢n m s significativa del plano de forma rápida, sencilla y estandarizada por parte del usuario.
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Josep Llados, Ernest Valveny, Gemma Sanchez and Enric Marti. 2003. A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa. Proceedings of Pattern Recognition in Information Systems. ICEIS Press, 1–13.
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Josep Llados, Jaime Lopez-Krahe and Enric Marti. 1996. Hand drawn document understanding using the straight line Hough transform and graph matching. Proceedings of the 13th International Pattern Recognition Conference (ICPR’96). Vienna , Austria, 497–501.
Abstract: This paper presents a system to understand hand drawn architectural drawings in a CAD environment. The procedure is to identify in a floor plan the building elements, stored in a library of patterns, and their spatial relationships. The vectorized input document and the patterns to recognize are represented by attributed graphs. To recognize the patterns as such, we apply a structural approach based on subgraph isomorphism techniques. In spite of their value, graph matching techniques do not recognize adequately those building elements characterized by hatching patterns, i.e. walls. Here we focus on the recognition of hatching patterns and develop a straight line Hough transform based method in order to detect the regions filled in with parallel straight fines. This allows not only to recognize filling patterns, but it actually reduces the computational load associated with the subgraph isomorphism computation. The result is that the document can be redrawn by editing all the patterns recognized
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Josep Llados, Enric Marti and Jaime Lopez-Krahe. 1999. A Hough-based method for hatched pattern detection in maps and diagrams. Proceeding of the Fifth Int. Conf. Document Analysis and Recognition ICDAR ’99.479–482.
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.
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Gemma Sanchez, Josep Llados and Enric Marti. 1997. Segmentation and analysis of linial texture in plans. Intelligence Artificielle et Complexité.. Paris.
Abstract: The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph.
Keywords: Structural Texture, Voronoi, Hierarchical Clustering, String Matching.
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Ernest Valveny and Enric Marti. 2001. Learning of structural descriptions of graphic symbols using deformable template matching. Proc. Sixth Int Document Analysis and Recognition Conf.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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