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Ernest Valveny; Philippe Dosch |
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A General Framework for the Evaluation of Symbol Recognition Methods |
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2007 |
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International Journal on Document Analysis and Recognition, vol. 9(1), pp 59–74 |
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DAG @ dag @ VaD2007 |
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893 |
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Josep Llados; J. Lopez-Krahe; D. Archambault |
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Special Issue on Information Technologies for Visually Impaired People |
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2007 |
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Novatica |
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186 |
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4-7 |
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DAG @ dag @ LLA2007a |
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903 |
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Miquel Ferrer; Ernest Valveny; F. Serratosa |
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Median graph: A new exact algorithm using a distance based on the maximum common subgraph |
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2009 |
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Pattern Recognition Letters |
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PRL |
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30 |
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5 |
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579–588 |
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Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems. |
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Elsevier Science Inc. |
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0167-8655 |
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DAG @ dag @ FVS2009a |
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1114 |
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Josep Llados; Dimosthenis Karatzas; Joan Mas; Gemma Sanchez |
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A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives |
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2008 |
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Journal of Universal Computer Science |
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14 |
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18 |
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2912–2935 |
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Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition |
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DAG @ dag @ LKM2008 |
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1142 |
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Marçal Rusiñol; Josep Llados; Gemma Sanchez |
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Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings |
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2010 |
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Pattern Analysis and Applications |
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PAA |
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13 |
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3 |
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321-331 |
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In this paper, we address the problem of symbol spotting in technical document images applied to scanned and vectorized line drawings. Like any information spotting architecture, our approach has two components. First, symbols are decomposed in primitives which are compactly represented and second a primitive indexing structure aims to efficiently retrieve similar primitives. Primitives are encoded in terms of attributed strings representing closed regions. Similar strings are clustered in a lookup table so that the set median strings act as indexing keys. A voting scheme formulates hypothesis in certain locations of the line drawing image where there is a high presence of regions similar to the queried ones, and therefore, a high probability to find the queried graphical symbol. The proposed approach is illustrated in a framework consisting in spotting furniture symbols in architectural drawings. It has been proved to work even in the presence of noise and distortion introduced by the scanning and raster-to-vector processes. |
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Springer-Verlag |
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1433-7541 |
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DAG @ dag @ RLS2010 |
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1165 |
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