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Author |
Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo |
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Title |
Feature Selection Based on Reinforcement Learning for Object Recognition |
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Conference Article |
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Year |
2012 |
Publication |
Adaptive Learning Agents Workshop |
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Pages |
33-39 |
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Address |
Valencia |
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ALA |
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Notes |
ADAS; RV |
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no |
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Call Number |
Admin @ si @ PSL2012 |
Serial |
2018 |
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Author |
German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez |
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Title |
Visual SLAM for Driverless Cars: A Brief Survey |
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Conference Article |
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Year |
2012 |
Publication |
IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles |
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Keywords |
SLAM |
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Address |
Alcalá de Henares |
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IVW |
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ADAS |
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no |
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Call Number |
Admin @ si @ RSP2012; ADAS @ adas |
Serial |
2019 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
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Pages |
1138-1143 |
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Abstract |
Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow. |
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Address |
Alcalá de Henares |
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Publisher |
IEEE Xplore |
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ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
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Conference |
IV |
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ADAS |
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no |
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Call Number |
Admin @ si @ NaS2012 |
Serial |
2020 |
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Author |
Miguel Oliveira; Angel Sappa; V. Santos |
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Title |
Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
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Pages |
299-303 |
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The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
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Address |
Alcalá de Henares |
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Publisher |
IEEE Xplore |
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ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
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Conference |
IV |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ OSS2012b |
Serial |
2021 |
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Author |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
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Title |
Articulated Particle Filter for Hand Tracking |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
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Pages |
3581 - 3585 |
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This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
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Address |
Tsukuba Science City, Japan |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RMG2012 |
Serial |
2031 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Unsupervised co-segmentation through region matching |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
749-756 |
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Abstract |
Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database. |
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Address |
Providence, Rhode Island |
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IEEE Xplore |
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ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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Conference |
CVPR |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ RSL2012b; ADAS @ adas @ |
Serial |
2033 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Multiple target tracking and identity linking under split, merge and occlusion of targets and observations |
Type |
Conference Article |
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Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
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Address |
Algarve, Portugal |
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ICPRAM |
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ADAS |
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no |
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Call Number |
Admin @ si @ RSL2012c; ADAS @ adas |
Serial |
2034 |
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Author |
German Ros; J. Guerrero; Angel Sappa; Antonio Lopez |
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Title |
VSLAM pose initialization via Lie groups and Lie algebras optimization |
Type |
Conference Article |
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Year |
2013 |
Publication |
Proceedings of IEEE International Conference on Robotics and Automation |
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Pages |
5740 - 5747 |
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Keywords |
SLAM |
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Abstract |
We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm. |
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Address |
Karlsruhe; Germany; May 2013 |
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ISSN |
1050-4729 |
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978-1-4673-5641-1 |
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Conference |
ICRA |
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Notes |
ADAS; 600.054; 600.055; 600.057 |
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no |
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Admin @ si @ RGS2013a; ADAS @ adas @ |
Serial |
2225 |
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Author |
David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
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Title |
Integrating Visual and Textual Cues for Query-by-String Word Spotting |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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511 - 515 |
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Abstract |
In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances. |
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Address |
Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; ADAS; 600.045; 600.055; 600.061 |
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no |
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Call Number |
Admin @ si @ ART2013 |
Serial |
2224 |
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Author |
Karel Paleček; David Geronimo; Frederic Lerasle |
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Title |
Pre-attention cues for person detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
Cognitive Behavioural Systems, COST 2102 International Training School |
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Pages |
225-235 |
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Abstract |
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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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-34583-8 |
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COST-TS |
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ADAS |
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no |
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Call Number |
Admin @ si @ PGL2012 |
Serial |
2148 |
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