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Marçal Rusiñol; Josep Llados; Gemma Sanchez |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings |
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Journal Article |
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Year |
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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Abstract |
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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Author |
Marçal Rusiñol; Josep Llados |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices |
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Journal Article |
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Year |
2009 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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12 |
Issue |
2 |
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83-96 |
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Keywords |
Performance evaluation; Symbol Spotting; Graphics Recognition |
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Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors. |
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1433-2833 |
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DAG @ dag @ RuL2009a |
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1166 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Median Graphs: A Genetic Approach based on New Theoretical Properties |
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Journal Article |
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2009 |
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Pattern Recognition |
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PR |
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42 |
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9 |
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2003–2012 |
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Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition |
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Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. |
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DAG @ dag @ FVS2009b |
Serial |
1167 |
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Author |
Marçal Rusiñol; Agnes Borras; Josep Llados |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images |
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Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition Letters |
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PRL |
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31 |
Issue |
3 |
Pages |
188–201 |
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Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings |
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This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results. |
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Elsevier |
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Notes ![sorted by Notes field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
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no |
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DAG @ dag @ RBL2010 |
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1177 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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Journal Article |
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Year |
2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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Volume |
31 |
Issue |
9 |
Pages |
1630–1644 |
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Abstract |
The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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no |
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Call Number |
DAG @ dag @ RVT2009 |
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1220 |
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