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Gemma Sanchez, Josep Llados, & K. Tombre. (2002). A mean string algorithm to compute the average among a set of 2D shapes. PRL - Pattern Recognition Letters, 23(1-3), 203–214.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Oriol Ramos Terrades, Ernest Valveny, & Salvatore Tabbone. (2009). Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9), 1630–1644.
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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Marçal Rusiñol, & Josep Llados. (2009). A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices. IJDAR - International Journal on Document Analysis and Recognition, 12(2), 83–96.
Abstract: 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.
Keywords: Performance evaluation; Symbol Spotting; Graphics Recognition
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