http://hdl.handle.net/2117/3757 2013-05-20T01:04:11Z http://hdl.handle.net/2117/11613 Title: PLC Control and Matlab/Simulink Simulations – A Translation Approach Authors: Martins, Joao; Lima, Celson; Grau Saldes, Antoni; Martínez García, Herminio 2011-03-01T18:03:48Z http://hdl.handle.net/2117/9689 Title: Enginyeria Tècnica Industrial, Especialitats en Electricitat, Electrònica, Mecànica i Química, a l'Escola Universitària d'Enginyeria Tècnica Industrial de Barcelona de la Universitat Politècnica de Catalunya Authors: Martínez García, Herminio; Jorba Peiró, Jordi; Gámiz Caro, Juan 2010-10-14T10:20:38Z http://hdl.handle.net/2117/9181 Title: Local boosted features for pedestrian detection Authors: Villamizar Vergel, Michael Alejandro; Sanfeliu Cortés, Alberto; Andrade-Cetto, Juan Abstract: The 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 2010-09-29T17:15:00Z http://hdl.handle.net/2117/7811 Title: A recursive embedding approach to median graph computation Authors: Ferrer Sumsi, Miquel; Valveny, Ernest; Bunke, Horst Abstract: The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches. 2010-06-23T10:24:44Z http://hdl.handle.net/2117/7485 Title: Graph-based k-means clustering: A comparison of the set versus the generalized median graph Authors: Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc; Bardaji Goikoetxea, Itziar; Bunke, Horst Abstract: In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means 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. 2010-06-02T12:49:21Z http://hdl.handle.net/2117/7107 Title: Median graph computation by means of a genetic approach based on minimum common supergraph and maximum common subraph Authors: Ferrer Sumsi, Miquel; Valveny, Ernest; Serratosa Casanelles, Francesc Abstract: 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 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 only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. 2010-05-03T13:01:33Z http://hdl.handle.net/2117/2683 Title: Unidimensional multiscale local features for object detection under rotation and mild occlusions Authors: Villamizar Vergel, Michael Alejandro; Sanfeliu Cortés, Alberto; Andrade-Cetto, Juan Abstract: In this article, scale and orientation invariant object detection is performed by matching intensity level histograms. Unlike other global measurement methods, the present one uses a local feature description that allows small changes in the histogram signature, giving robustness to partial occlusions. Local features over the object histogram are extracted during a Boosting learning phase, selecting the most discriminant features within a training histogram image set. The Integral Histogram has been used to compute local histograms in constant time. 2009-03-13T09:21:10Z http://hdl.handle.net/2117/2682 Title: Robust color contour object detection invariant to shadows Authors: Scandaliaris, Jorge; Villamizar Vergel, Michael Alejandro; Andrade-Cetto, Juan; Sanfeliu Cortés, Alberto Abstract: In this work a new robust color and contour based object detection method in images with varying shadows is presented. The method relies on a physics-based contour detector that emphasizes material changes and a contour-based boosted classifier. The method has been tested in a sequence of outdoor color images presenting varying shadows using two classifiers, one that learns contour object features from a simple gradient detector, and another that learns from the photometric invariant contour detector. It is shown that the detection performance of the classifier trained with the photometric invariant detector is significantly higher than that of the classifier trained with gradient detector. 2009-03-13T09:21:01Z http://hdl.handle.net/2117/2668 Title: A new algorithm to compute the distance between multi-dimensional histograms Authors: Serratosa Casanelles, Francesc; Sanromà Güell, Gerard; Sanfeliu Cortés, Alberto Abstract: The aim of this paper is to present a new algorithm to compute the distance between ndimensional histograms. There are some domains such as pattern recognition or image retrieval that use the distance between histograms at some step of the classification process. For this reason, some algorithms that find the distance between histograms have been proposed in the literature. Nevertheless, most of this research has been applied on one-dimensional histograms due to the computation of a distance between multi-dimensional histograms is very expensive. In this paper, we present an efficient method to compare multi dimensional histograms in O(z2), where z represents the number of bins. 2009-03-13T09:18:37Z http://hdl.handle.net/2117/2666 Title: Real-time software for mobile robot simulation and experimentation in cooperative environments Authors: Corominas Murtra, Andreu; Mirats Tur, Josep Maria; Sandoval Torres, Óscar; Sanfeliu Cortés, Alberto Abstract: This paper presents the software being developed at IRI (Institut de Robotica i Informatica Industrial) for mobile robot autonomous navigation in the context of the European project URUS (Ubiquitous Robots in Urban Settings). In order that a deployed sensor network and robots operating in the environment cooperate in terms of information sharing, main requirements are real-time performance and the integration of information coming from remote machines not onboard the robot. Moreover, the project involves a group of eleven industrial and academic partners, therefore software integration issues are critical. The proposed software framework is based on the YARP middleware and has been tested in real and simulated experiments. 2009-03-13T09:18:18Z