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Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Using the Average Landmark Vector Method for Robot Homing |
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Conference Article |
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2007 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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Admin @ si @ GRL2007 |
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899 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Class-Specific Binaryy Correlograms for Object Recognition |
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Conference Article |
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2007 |
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British Machine Vision Conference |
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Warwick (UK) |
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BMVC’07 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2007a |
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923 |
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Author |
Karel Paleček; David Geronimo; Frederic Lerasle |
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Title |
Pre-attention cues for person detection |
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Conference Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Cognitive Behavioural Systems, COST 2102 International Training School |
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225-235 |
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Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector. |
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Dresden, Germany |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-34583-8 |
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Admin @ si @ PGL2012 |
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2148 |
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Author |
Angel Sappa; Rosa Herrero; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Road Approximation in Euclidean and v-Disparity Space: A Comparative Study |
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Conference Article |
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2007 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Computer Aided Systems Theory, |
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4739 |
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1105–1112 |
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This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge. |
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Las Palmas de Gran Canaria (Spain) |
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EUROCAST |
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ADAS |
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ADAS @ adas @ SHD2007b |
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917 |
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Author |
Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Efficient On-Board Stereo Vision Pose Estimation |
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Conference Article |
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2007 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Computer Aided Systems Theory, Selected paper from |
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4739 |
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1183–1190 |
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This paper presents an efficient technique for real time estimation of on-board stereo vision system pose. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the 3D road points. Fast RANSAC fitting is obtained by selecting points according to a probability distribution function that takes into account the density of points at a given depth. Finally, stereo camera position
and orientation—pose—is computed relative to the road plane. The proposed technique is intended to be used on driver assistance systems for applications such as obstacle or pedestrian detection. A real time performance is reached. Experimental results on several environments and comparisons with a previous work are presented. |
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Las Palmas de Gran Canaria (Spain) |
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EUROCAST |
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ADAS |
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ADAS @ adas @ SDG2007b |
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916 |
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Author |
Joan Serrat; Jordi Vitria; J. Pladellorens |
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Title |
Morphological Segmentation of Heart Scintigraphic image Sequences. |
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Conference Article |
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1991 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Computer Assisted Radiology. |
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Berlin |
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ADAS;OR;MV |
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ADAS @ adas @ SVP1991 |
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263 |
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Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez |
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Title |
Action-Based Representation Learning for Autonomous Driving |
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Conference Article |
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2020 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Conference on Robot Learning |
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Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). |
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virtual; November 2020 |
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CORL |
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ADAS; 600.118 |
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Admin @ si @ XCP2020 |
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3487 |
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J. Mauri; Eduard Fernandez-Nofrerias; B. Garcia del Blanco; E. Iraculis; J.A. Gomez-Hospital; J. Comin; M.A. Sanchez Corral; F. Jara; A. Cequier; E. Esplugas; Debora Gil; J. Saludes; Petia Radeva; Ricardo Toledo; Juan J.Villanueva |
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Title |
Moviment del vas en l anàlisi d imatges d ecografia intracoronària: un model matemàtic |
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Conference Article |
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2000 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Congrés de la Societat Catalana de Cardiologia. |
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IAM;RV;ISE;MILAB;ADAS |
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IAM @ iam @ MNG2000 |
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1621 |
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J. Mauri; Eduard Fernandez-Nofrerias; J. Comin; B. Garcia del Blanco; E. Iraculis; J.A. Gomez-Hospital; P. Valdovinos; F. Jara; A. Cequier; E. Esplugas; Oriol Pujol; Cristina Cañero; Debora Gil; Petia Radeva; Ricardo Toledo |
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Title |
Avaluació del Conjunt Stent/Artèria mitjançant ecografia intracoronària: lentorn informàtic |
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Conference Article |
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2000 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Congrés de la Societat Catalana de Cardiologia. |
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IAM;RV;MILAB;ADAS;HuPBA |
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IAM @ iam @ MNC2000 |
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1622 |
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David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
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Title |
Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes |
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Conference Article |
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2013 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
CVPR Workshop on Ground Truth – What is a good dataset? |
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706 - 711 |
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Pedestrian Detection; Domain Adaptation |
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Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. |
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Portland; Oregon; June 2013 |
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IEEE |
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English |
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English |
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CVPRW |
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ADAS; 600.054; 600.057; 601.217 |
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ADAS @ adas @ VXR2013a |
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2219 |
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