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Alicia Fornes, Sergio Escalera, Josep Llados and Ernest Valveny. 2010. Symbol Classification using Dynamic Aligned Shape Descriptor. 20th International Conference on Pattern Recognition.1957–1960.
Abstract: Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates.
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Anjan Dutta. 2010. Symbol Spotting in Graphical Documents by Serialized Subgraph Matching. (Master's thesis, .)
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Anjan Dutta, Umapada Pal, Alicia Fornes and Josep Llados. 2010. An Efficient Staff Removal Technique from Printed Musical Documents. 20th International Conference on Pattern Recognition.1965–1968.
Abstract: Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in for evaluation purpose. From experimentation we have got encouraging results.
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Antonio Clavelli, Dimosthenis Karatzas and Josep Llados. 2010. A framework for the assessment of text extraction algorithms on complex colour images. 9th IAPR International Workshop on Document Analysis Systems.19–26.
Abstract: The availability of open, ground-truthed datasets and clear performance metrics is a crucial factor in the development of an application domain. The domain of colour text image analysis (real scenes, Web and spam images, scanned colour documents) has traditionally suffered from a lack of a comprehensive performance evaluation framework. Such a framework is extremely difficult to specify, and corresponding pixel-level accurate information tedious to define. In this paper we discuss the challenges and technical issues associated with developing such a framework. Then, we describe a complete framework for the evaluation of text extraction methods at multiple levels, provide a detailed ground-truth specification and present a case study on how this framework can be used in a real-life situation.
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David Fernandez. 2010. Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors. (Master's thesis, .)
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Farshad Nourbakhsh, Dimosthenis Karatzas and Ernest Valveny. 2010. A polar-based logo representation based on topological and colour features. 9th IAPR International Workshop on Document Analysis Systems.341–348.
Abstract: In this paper, we propose a novel rotation and scale invariant method for colour logo retrieval and classification, which involves performing a simple colour segmentation and subsequently describing each of the resultant colour components based on a set of topological and colour features. A polar representation is used to represent the logo and the subsequent logo matching is based on Cyclic Dynamic Time Warping (CDTW). We also show how combining information about the global distribution of the logo components and their local neighbourhood using the Delaunay triangulation allows to improve the results. All experiments are performed on a dataset of 2500 instances of 100 colour logo images in different rotations and scales.
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Herve Locteau, Sebastien Mace, Ernest Valveny and Salvatore Tabbone. 2010. Extraction des pieces de un plan de habitation. Colloque Internacional Francophone de l´Ecrit et le Document.1–12.
Abstract: In this article, a method to extract the rooms of an architectural floor plan image is described. We first present a line detection algorithm to extract long lines in the image. Those lines are analyzed to identify the existing walls. From this point, room extraction can be seen as a classical segmentation task for which each region corresponds to a room. The chosen resolution strategy consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines can also be rough. Thus, we take advantage of knowledge associated to architectural floor plans in order to obtain mainly rectangular rooms. Preliminary tests on a set of real documents show promising results.
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Jaume Gibert and Ernest Valveny. 2010. Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. In In E.R. Hancock, R.C.W., T. Windeatt, I. Ulusoy and F. Escolano,, ed. 13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition. Springer Berlin Heidelberg, 223–232. (LNCS.)
Abstract: Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster.
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Jaume Gibert, Ernest Valveny and Horst Bunke. 2010. Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis. 15th Iberoamerican Congress on Pattern Recognition.30–37. (LNCS.)
Abstract: Structure-Activity relationship analysis aims at discovering chemical activity of molecular compounds based on their structure. In this article we make use of a particular graph representation of molecules and propose a new graph embedding procedure to solve the problem of structure-activity relationship analysis. The embedding is essentially an arrangement of a molecule in the form of a vector by considering frequencies of appearing atoms and frequencies of covalent bonds between them. Results on two benchmark databases show the effectiveness of the proposed technique in terms of recognition accuracy while avoiding high operational costs in the transformation.
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Jean-Marc Ogier, Wenyin Liu and Josep Llados, eds. 2010. Graphics Recognition: Achievements, Challenges, and Evolution. Springer Link. (LNCS.)
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