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Enric Marti; Xavier Binefa; G.EstapeRV |
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Caronte, plataforma para la gestión de la actividad docente de una asignatura. Análisis de su impacto en ingenierías, para su adaptación al EEES |
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Miscellaneous |
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2008 |
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, Ministerio de Ciencia e Innovacion, DGU |
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Direccion General de Universidades |
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IAM;OR;RV |
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IAM @ iam @ MBE2008 |
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1589 |
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Author |
Fernando Vilariño; Enric Marti |
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Title |
New didactic techniques in the EHES applying mobile technologies |
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Miscellaneous |
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2008 |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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MILAB;IAM;MV;SIAI |
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IAM @ iam @ VIM2008 |
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1664 |
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Author |
Enric Marti; Petia Radeva; Ricardo Toledo; Jordi Vitria |
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Title |
Experiencia de aplicación de la metodología de aprendizaje por proyectos en asignaturas de Ingeniería Informática para una mejor adaptación a los créditos ECTS i al Espacio Europeo de Educación Superior |
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Miscellaneous |
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2005 |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR) |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR) |
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IAM;RV;OR;MILAB;ADAS;MV |
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IAM @ iam @ MRT2005 |
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1608 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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Conference Article |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Abstract |
Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
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Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
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Conference Article |
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2015 |
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IEEE Winter Conference on Applications of Computer Vision |
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231 - 238 |
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Keywords |
Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
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Abstract |
Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
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Hawaii; January 2015 |
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WACV |
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ADAS; 600.076 |
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ADAS @ adas @ RRG2015 |
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2499 |
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Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez |
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Title |
Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection |
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Conference Article |
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Year |
2015 |
Publication |
IEEE Intelligent Vehicles Symposium IV2015 |
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356-361 |
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Keywords |
Pedestrian Detection |
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Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. |
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Seoul; Corea; June 2015 |
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IV |
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ADAS; 600.076; 600.057; 600.054 |
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ADAS @ adas @ GVX2015 |
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2625 |
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Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez |
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Title |
3D-Guided Multiscale Sliding Window for Pedestrian Detection |
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Conference Article |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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9117 |
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560-568 |
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Keywords |
Pedestrian Detection |
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The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.076; 600.057; 600.054 |
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ADAS @ adas @ GVR2015 |
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2585 |
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Author |
Ole Larsen; Petia Radeva; Enric Marti |
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Title |
Calculating the Bounds on the Optimal Parameters of Elasticity for a Snake |
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Report |
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1994 |
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Technical Report |
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Aalborg University |
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Aalborg University, Laboratory of image Analysis. |
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Denmark |
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Aalborg University, Laboratory of image Analysis. |
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MILAB;IAM |
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IAM @ iam @ LRM1994 |
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1560 |
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Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
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Journal Article |
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2006 |
Publication |
Pattern Recognition Letters |
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PRL |
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27 |
Issue |
8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Abstract |
Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Author |
Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions |
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Book Chapter |
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2006 |
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9th International Conference on Medical Image Computing and Computer–Assisted Intervention |
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4191 |
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161–168 |
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Abstract |
Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions. |
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Copenhagen (Denmark) |
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Springer Verlag |
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Berlin Heidelberg |
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R. Larsen, M. Nielsen, and J. Sporring |
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LNCS |
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800 |
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MICCAI06 |
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MV;OR;MILAB;SIAI |
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BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 |
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725 |
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