
Joan Serrat, & Enric Marti. (1991)." Elastic matching using interpolation splines" In IV Spanish Symposium of Pattern Recognition and image Analysis.



Ernest Valveny, & Enric Marti. (2001). "Learning of structural descriptions of graphic symbols using deformable template matching " In Proc. Sixth Int Document Analysis and Recognition Conf (pp. 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.



Ernest Valveny, & Enric Marti. (2000). "Handdrawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework " In Proc. 15th Int Pattern Recognition Conf (Vol. 2, pp. 239–242).
Abstract: Handdrawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in handdrawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm.



Ernest Valveny, & Enric Marti. (1999). "Application of deformable template matching to symbol recognition in handwritten architectural draw " In Proceedings of the Fifth International Conference on. Bangalore (India).
Abstract: We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in handwritten line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to handwritten architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified.



Ernest Valveny, & Enric Marti. (1999)." Recognition of lineal symbols in handwritten drawings using deformable template matching" In Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes.



Ernest Valveny, & Enric Marti. (1997)." Dimensions analysis in handdrawn architectural drawings" In VII National Simposium of Pattern Recognition and image Analysis, SNRFAI´97 (pp. 90–91). CVCUAB.



Ernest Valveny, Ricardo Toledo, Ramon Baldrich, & Enric Marti. (2002)." Combining recognitionbased in segmentationbased approaches for graphic symol recognition using deformable template matching" In Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002 (502–507).



Aura HernandezSabate, Debora Gil, David Roche, Monica M. S. Matsumoto, & Sergio S. Furuie. (2011). "Inferring the Performance of Medical Imaging Algorithms " In Pedro Real, Daniel DiazPernil, Helena MolinaAbril, Ainhoa Berciano, & Walter Kropatsch (Eds.), 14th International Conference on Computer Analysis of Images and Patterns (Vol. 6854, pp. 520–528). L. Berlin: SpringerVerlag Berlin Heidelberg.
Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.



David Roche, Debora Gil, & Jesus Giraldo. (2011). "An inference model for analyzing termination conditions of Evolutionary Algorithms " In 14th Congrès Català en Intel·ligencia Artificial (pp. 216–225).
Abstract: In realworld problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distributionbased quantities are good candidates as far as suitable parameters are used. A main limitation for application to realworld problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distributionbased quantity asymptotically behaves like the solution accuracy. We present a regressionprediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distributionbased quantities influence their asymptotic behavior. Second, to what extent existing distributionbased quantities can be asymptotically exchanged for the accuracy of the EA solution.
Keywords: Evolutionary Computation Convergence, Termination Conditions, Statistical Inference



David Roche, Debora Gil, & Jesus Giraldo. (2011). "Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms " In 11th European Conference on Artificial Life.
Abstract: A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in realworld applications. Although for known test functions distributionbased quantities are good candidates (as far as suitable parameters are used), in realworld problems an open question still remains unsolved. How can we estimate an upperbound for the termination condition value ensuring a given accuracy for the (unknown) EA solution?
We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output.

