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Adaptation of a computer programming course to the ESHE requirements: evaluation five years later
Valveny, Ernest; Benavente, Robert; Lapedriza, A.; Ferrer Sumsi, Miquel; GarciaBarnés, Jaume; Sànchez, Gemma
European journal of engineering education
Date of publication: 2012
Journal article
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Generalized median string computation by means of string embedding in vector spaces
Jiang, Xiaoyi; Wentker, Jöran; Ferrer Sumsi, Miquel
Pattern recognition letters
Date of publication: 20121101
Journal article
Read the abstract View Share Reference managersIn structural pattern recognition the median string has been established as a useful tool to represent a set of strings. However, its exact computation is complex and of high computational burden. In this paper we propose a new approach for the computation of median string based on string embedding. Strings are embedded into a vector space and the median is computed in the vector domain. We apply three different inverse transformations to go from the vector domain back to the string domain in order to obtain a final approximation of the median string. All of them are based on the weighted mean of a pair of strings. Experiments show that we succeed to compute good approximations of the median string.
In structural pattern recognition the median string has been established as a useful tool to represent a set of strings. However, its exact computation is complex and of high computational burden. In this paper we propose a new approach for the computation of median string based on string embedding. Strings are embedded into a vector space and the median is computed in the vector domain. We apply three different inverse transformations to go from the vector domain back to the string domain in order to obtain a final approximation of the median string. All of them are based on the weighted mean of a pair of strings. Experiments show that we succeed to compute good approximations of the median string. 
Linked Data Benchmark Council
Perez Casany, Marta; Martinez Bazan, Norbert; Escale Claveras, Francesc; Ferrer Sumsi, Miquel; Prat Perez, Arnau; Dominguez Sal, David; Larriba Pey, Josep
Participation in a competitive project
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Editorial for the special issue on graphbased representations in pattern recognition
Torsello, Andrea; Jiang, Xiaoyi; Ferrer Sumsi, Miquel
Pattern recognition letters
Date of publication: 2012
Journal article
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A generic framework for median graph computation based on a recursive embedding approach
Ferrer Sumsi, Miquel; Karatzas, D; Valveny, Ernest; Bardaji Goikoetxea, Itziar; Bunke, Horst
Computer vision and image understanding
Date of publication: 2011
Journal article
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A comparison between two representatives of a set of graphs: median vs barycenter graph
Bardaji Goikoetxea, Itziar; Ferrer Sumsi, Miquel; Sanfeliu Cortes, Alberto
International Workshop on Structural and Syntactic Pattern Recognition
Presentation's date: 2010
Presentation of work at congresses
Read the abstract Access to the full text Share Reference managersIn this paper we consider two existing methods to generate a representative of a given set of graphs, that satisfy the following two conditions. On the one hand, that they are applicable to graphs with any kind of labels in nodes and edges and on the other hand, that they can handle relatively large amount of data. Namely, the approximated algorithms to compute the Median Graph via graph embedding and a new method to compute the Barycenter Graph. Our contribution is to give a new algorithm for the barycenter computation and to compare it to the median Graph. To compare these two representatives, we take into account algorithmic considerations and experimental results on the quality of the representative and its robustness, on several datasets. 
Autonomous navigation for urban service mobile robots
Corominas Murtra, Andreu; Trulls Fortuny, Eduard; Sandoval Torres, Oscar; Perez Ibarz, Joan; Vasquez, Dizan; Mirats Tur, Josep Maria; Ferrer Sumsi, Miquel; Sanfeliu Cortes, Alberto
IEEE/RSJ International Conference on Intelligent Robots and Systems
Presentation's date: 2010
Presentation of work at congresses
Read the abstract Access to the full text Share Reference managersWe present to the robotic community a fully autonomous navigation solution for mobile robots operating in urban pedestrian areas. We introduce our robots and the experimental zone, overview the architecture of the navigation framework, and present the results after 3.5km of autonomous navigation. We expose the main lessons learnt by the scientific team and identify the issues to improve future works.
Postprint (author’s final draft) 
Computing the barycenter graph by means of the graph edit distance
Bardaji Goikoetxea, Itziar; Ferrer Sumsi, Miquel; Sanfeliu Cortes, Alberto
International Conference on Pattern Recognition
Presentation's date: 2010
Presentation of work at congresses
Read the abstract Access to the full text Share Reference managersThe barycenter graph has been shown as an alternative to obtain the representative of a given set of graphs. In this paper we propose an extension of the original algorithm which makes use of the graph edit distance in conjunction with the weighted mean of a pair of graphs. Our main contribution is that we can apply the method to attributed graphs with any kind of labels in both the nodes and the edges, equipped with a distance function less constrained than in previous approaches. Experiments done on four different datasets support the validity of the method giving good approximations of the barycenter graph.
Postprint (author’s final draft) 
A conductance electrical model for representing and matching weighted undirected graphs
Igelmo Ganzo, Manuel; Sanfeliu Cortes, Alberto; Ferrer Sumsi, Miquel
International Conference on Pattern Recognition
Presentation's date: 2010
Presentation of work at congresses
Read the abstract Access to the full text Share Reference managersIn this paper we propose a conductance electrical model to represent weighted undirected graphs that allows us to efficiently compute approximate graph isomorphism in large graphs. The model is built by transforming a graph into an electrical circuit. Edges in the graph become conductances in the electrical circuit. This model follows the laws of the electrical circuit theory and we can potentially use all the existing theory and tools of this field to derive other approximate techniques for graph matching. In the present work, we use the proposed circuital model to derive approximated graph isomorphism solutions. 
An iterative algorithm for approximate median graph computation
Ferrer Sumsi, Miquel; Bunke, Horst
International Conference on Pattern Recognition
Presentation's date: 2010
Presentation of work at congresses
Read the abstract View Share Reference managersRecently, the median graph has been shown to be a good choice to obtain a representative of a given set of graphs. It has been successfully applied to graphbased classification and clustering. In this paper we exploit a theoretical property of the median, which has not yet been utilized in the past, to derive a new iterative algorithm for approximate median graph computation. Experiments done using five different graph databases show that the proposed approach yields, in four out of these five datasets, better medians than two of the previous existing methods.
Recently, the median graph has been shown to be a good choice to obtain a representative of a given set of graphs. It has been successfully applied to graphbased classification and clustering. In this paper we exploit a theoretical property of the median, which has not yet been utilized in the past, to derive a new iterative algorithm for approximate median graph computation. Experiments done using five different graph databases show that the proposed approach yields, in four out of these five datasets, better medians than two of the previous existing methods. 
Generalized median graph computation by means of graph embedding in vector spaces
Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc; Riesen, Kaspar; Bunke, Horst
Pattern recognition
Date of publication: 2010
Journal article
Read the abstract View Share Reference managersThe median graph has been presented as a useful tool to represent a set of graphs. Nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data. In this paper we propose a new approach for the computation of the median graph based on graph embedding. Graphs are embedded into a vector space and the median is computed in the vector domain. We have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph. Experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. These experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed. 
Median graphs: a genetic approach based on new theoretical properties
Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc
Pattern recognition
Date of publication: 2009
Journal article
Read the abstract View Share Reference managersGiven 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 onlytheoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. 
Median graph: a new exact algorithm using a distance based on the maximum common subgraph
Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc
Pattern recognition letters
Date of publication: 2009
Journal article
Read the abstract View Share Reference managersMedian 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. 
Graphbased kmeans clustering: A comparison of the set versus the generalized median graph
Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc; Bardaji Goikoetxea, Itziar; Bunke, Horst
Date of publication: 2009
Book chapter
Read the abstract View Share Reference managersIn this paper we propose the application of the generalized median graph in a graphbased kmeans clustering algorithm. In the graphbased kmeans algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. 
Median graph computation by means of a genetic approach based on minimum common supergraph and maximum common subraph
Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc
Date of publication: 2009
Book chapter
Read the abstract View Share Reference managersGiven 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 a new genetic algorithm for the median graph computation. 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, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the onlytheoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. 
A recursive embedding approach to median graph computation
Ferrer Sumsi, Miquel; Karatzas, D; Valveny, Ernest; Bunke, Horst
Date of publication: 20090612
Book chapter
Read the abstract Access to the full text Share Reference managersThe present paper addresses pedestrian detection using local boosted features that are learned from a small set of training images. Our contribution is to use two boosting steps. The first one learns discriminant local features corresponding to pedestrian parts and the second one selects and combines these boosted features into a robust class classifier. In contrast of other works, our features are based on local differences over Histograms of Oriented Gradients (HoGs). Experiments carried out to a public dataset of pedestrian images show good performance with high classification rates 
Programació en llenguatge C : amb 56 problemes resolts i comentats
Valveny, Ernest; Benavente, Robert; Lapedriza, A.; Ferrer Sumsi, Miquel; GarciaBarnés, Jaume
Date of publication: 2009
Book
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Aprendizaje cooperativo aplicado a la docencia de las asignaturas de programación en ingeniería informática
Ferrer Sumsi, Miquel; Benavente, Robert; Valveny, Ernest; GarciaBarnés, Jaume; Lapedriza, A.; Sànchez, Gemma
Jornada sobre Aprendizaje Cooperativo, Jornada sobre Innovación en la Docencia
Presentation's date: 2008
Presentation of work at congresses
Read the abstract Access to the full text Share Reference managersEn este trabajo se presentan los resultados de la aplicación de la metodología del aprendizaje cooperativo a la docencia de dos asignaturas de programación en ingeniería informática. `Algoritmos y programación¿ y `Lenguajes de programación¿ son dos asignaturas complementarias que se organizan entorno a un proyecto común que engloba los contenidos de ambas asignaturas. En la docencia de una parte muy importante de estas asignaturas, la metodología del aprendizaje cooperativo se ha adaptado a sus características específicas. Como muestra de esta adaptación presentamos dos ejemplos de las actividades desarrolladas dentro de la docencia de estas asignaturas. Después de tres años de aplicación, el análisis a nivel cualitativo y cuantitativo de los resultados muestra que éstos son muy satisfactorios y que la aplicación del método cooperativo ha mejorado de forma considerable el rendimiento de los alumnos en ambas asignaturas.
En este trabajo se presentan los resultados de la aplicación de la metodología del aprendizaje cooperativo a la docencia de dos asignaturas de programación en ingeniería informática. ‘Algoritmos y programación’ y ‘Lenguajes de programación’ son dos asignaturas complementarias que se organizan entorno a un proyecto común que engloba los contenidos de ambas asignaturas. En la docencia de una parte muy importante de estas asignaturas, la metodología del aprendizaje cooperativo se ha adaptado a sus características específicas. Como muestra de esta adaptación presentamos dos ejemplos de las actividades desarrolladas dentro de la docencia de estas asignaturas. Después de tres años de aplicación, el análisis a nivel cualitativo y cuantitativo de los resultados muestra que éstos son muy satisfactorios y que la aplicación del método cooperativo ha mejorado de forma considerable el rendimiento de los alumnos en ambas asignaturas. 
Application of graph embedding to solve graph matching problems
Valveny, Ernest; Ferrer Sumsi, Miquel
Colloque International Francophone sur l'Ecrit et le Document
Presentation's date: 200810
Presentation of work at congresses
Read the abstract View Share Reference managersGraphs have very interesting properties for object representation in pattern recognition. However, graph matching algorithms are usually computationally complex. In addition, graphs are harder to manipulate and operate than feature vectors. In the last years, some attempts have been made to combine the best of the graph and the vector domains in order to get the advantages of both worlds. In this paper we review some of these works on graph kernels and graph embedding and we show how graph embedding can be used to obtain accurate and efficient approximations of the median graph. The median graph can be seen as the representative of a set of graphs but its application has been very limited up to now due to computational reasons. With this new approach, we can obtain an approximate median graph using real databases containing large graphs.
Graphs have very interesting properties for object representation in pattern recognition. However, graph matching algorithms are usually computationally complex. In addition, graphs are harder to manipulate and operate than feature vectors. In the last years, some attempts have been made to combine the best of the graph and the vector domains in order to get the advantages of both worlds. In this paper we review some of these works on graph kernels and graph embedding and we show how graph embedding can be used to obtain accurate and efficient approximations of the median graph. The median graph can be seen as the representative of a set of graphs but its application has been very limited up to now due to computational reasons. With this new approach, we can obtain an approximate median graph using real databases containing large graphs. Motsclés : Graph Matching, Graph Embedding, Graph Kernels, Vector Spaces, Median Graph tors. Secondly, the repository of algorithmic tools based on graphs is quite limited when compared to the tools available for patterns represented using feature vectors. This is mainly due to the fact that vectors are simple structures with good mathematical properties that can be readily manipulated algebraically. For this reason, new trends in structural pattern recognition have been proposed merging both worlds in order to extend the available statistical tools to the graph domain [BUN 05]. In this way, graph kernels permit to compute the dot product of the representation of a pair of graphs in a vector space without having to define the explicit transformation between the graphs and the vector space. As a consequence all classification algorithms based on the computation of a dot product, such as Support Vector Machines (SVM) become immediately available for graphs. On the other hand, graph embedding aims to find an explicit transformation between graphs and a vector space. In this way, we can give a semantic interpretation to this transformation. In addition we can also manipulate the vectors resulting from this transformation with all the mathematical machinery that can be applied to vectors. We are not restricted to the dot product. In this paper, we firstly review the main techniques used to define graph kernels and graph embedding in sections 2 and 3, respectively. Then, in section 4 we show the application of graph embedding to a particular complex graph matching problem : the computation of the median graph. In this section we introduce the concept of median graph as a representative of a set of graphs and then, we describe how it can be efficiently computed using graph embedding. We also show some results of its application to real pattern recognition problems. Finally, in section 5 we state some conclusions and point out some challenges for the future. 
An approximate algorithm for median graph computation using graph embedding
Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc; Riesen, Kaspar; Bunke, Horst
International Conference on Pattern Recognition
Presentation's date: 2008
Presentation of work at congresses
Read the abstract View Share Reference managersGraphs are powerful data structures that have many attractive properties for object representation. However, some basic operations are difficult to define and implement, for instance, how to obtain a representative of a set of graphs. The median graph has been defined for that purpose, but existing algorithms are computationally complex and have a very limited applicability. In this paper we propose a new approach for the computation of the median graph based on graph embedding in vector spaces. Experiments on a real database containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results.
Graphs are powerful data structures that have many attractive properties for object representation. However, some basic operations are difficult to define and implement, for instance, how to obtain a representative of a set of graphs. The median graph has been defined for that purpose, but existing algorithms are computationally complex and have a very limited applicability. In this paper we propose a new approach for the computation of the median graph based on graph embedding in vector spaces. Experiments on a real database containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. 
Segmentation robust to the vignette effect for machine vision systems
Karatzas, D; Rusiñol, Marçal; Antens, Coen; Ferrer Sumsi, Miquel
International Conference on Pattern Recognition
Presentation's date: 2008
Presentation of work at congresses
Read the abstract View Share Reference managersThe vignette effect (radial falloff) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, nonlinear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect.
The vignette effect (radial falloff) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, nonlinear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect. 
Theory and algorihtms on the median graph. Application to graphbased classification and clustering
Ferrer Sumsi, Miquel
Date of publication: 2008
Book
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Combination of OCR engines for page segmentation based on performance evaluation
Ferrer Sumsi, Miquel; Valveny, Ernest
International Conference on Document Analysis and Recognition
Presentation's date: 2007
Presentation of work at congresses
Read the abstract View Share Reference managersIn this paper we present a method to improve the performance of individual page segmentation engines based on the combination of the output of several engines. The rules of combination are designed after analyzing the results of each individual method. This analysis is performed using a performance evaluation framework that aims at characterizing each method according to its strengths and weaknesses rather than computing a single performance measure telling which is the "best" segmentation method. 
CONSOLIDERINGENIO 2010 Multimodal interaction in pattern recognition and computer vision
Alquezar Mancho, Renato; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis; Ferrer Sumsi, Miquel
Participation in a competitive project
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Ubiquitous Network Robots in Urban Settings (IST045062)
Sanfeliu Cortes, Alberto; Ferrer Sumsi, Miquel
Participation in a competitive project
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