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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Video Co-segmentation |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th Asian Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7725 |
Issue |
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Pages |
13-24 |
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Abstract |
Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos. |
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Daejeon, Korea |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-37443-2 |
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Conference |
ACCV |
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Notes  |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RSL2012d |
Serial |
2153 |
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Author |
Monica Piñol; Angel Sappa; Ricardo Toledo |
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Title |
MultiTable Reinforcement for Visual Object Recognition |
Type |
Conference Article |
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Year |
2012 |
Publication |
4th International Conference on Signal and Image Processing |
Abbreviated Journal |
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Volume |
221 |
Issue |
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Pages |
469-480 |
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Abstract |
This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach. |
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Address |
Coimbatore, India |
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Publisher |
Springer India |
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LNCS |
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ISSN |
1876-1100 |
ISBN |
978-81-322-0996-6 |
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ICSIP |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ PST2012 |
Serial |
2157 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Non-Rigid Shape Registration: A Single Linear Least Squares Framework |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7578 |
Issue |
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Pages |
264-277 |
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Abstract |
This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided. |
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Address |
Florencia |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33785-7 |
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Conference |
ECCV |
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ADAS |
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no |
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Call Number |
Admin @ si @ RoS2012a |
Serial |
2158 |
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Author |
Miguel Oliveira; V.Santos; Angel Sappa |
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Title |
Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles |
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Address |
Algarve; Portugal |
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PPNIV |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ OSS2012c |
Serial |
2159 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
Type |
Conference Article |
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Year |
2013 |
Publication |
20th International Conference On Neural Information Processing |
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Volume |
8228 |
Issue |
III |
Pages |
368-375 |
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Abstract |
An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Address |
Daegu; Korea; November 2013 |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-42050-4 |
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Conference |
ICONIP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ DFD2013 |
Serial |
2439 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
DA-DPM Pedestrian Detection |
Type |
Conference Article |
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Year |
2013 |
Publication |
ICCV Workshop on Reconstruction meets Recognition |
Abbreviated Journal |
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Pages |
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Keywords |
Domain Adaptation; Pedestrian Detection |
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ICCVW-RR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ XRV2013 |
Serial |
2569 |
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Author |
Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez |
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Title |
Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning |
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Conference Article |
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Year |
2023 |
Publication |
International Conference on Intelligent Robots and Systems |
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Abstract |
On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning. |
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Detroit; USA; October 2023 |
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IROS |
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ADAS |
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no |
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Call Number |
Admin @ si @ XCP2023 |
Serial |
3930 |
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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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Volume |
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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 |
ISBN |
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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Call Number |
Admin @ si @ RGS2013a; ADAS @ adas @ |
Serial |
2225 |
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Author |
Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers |
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Title |
Adapting Pedestrian Detection from Synthetic to Far Infrared Images |
Type |
Conference Article |
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Year |
2013 |
Publication |
ICCV Workshop on Visual Domain Adaptation and Dataset Bias |
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Keywords |
Domain Adaptation; Far Infrared; Pedestrian Detection |
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Abstract |
We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. |
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Sydney; Australia; December 2013 |
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Place of Publication |
Sydney, Australy |
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Language |
English |
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ICCVW-VisDA |
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Notes |
ADAS; 600.054; 600.055; 600.057; 601.217;ISE |
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no |
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Call Number |
ADAS @ adas @ SRV2013 |
Serial |
2334 |
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Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
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Title |
Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection |
Type |
Conference Article |
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Year |
2013 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
467 - 472 |
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Keywords |
Pedestrian Detection; Virtual World; Part based |
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Abstract |
State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster). |
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Address |
Gold Coast; Australia; June 2013 |
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Publisher |
IEEE |
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ISSN |
1931-0587 |
ISBN |
978-1-4673-2754-1 |
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Conference |
IV |
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Notes |
ADAS; 600.054; 600.057 |
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no |
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Call Number |
XVL2013; ADAS @ adas @ xvl2013a |
Serial |
2214 |
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