| Records |
| Author |
Mohammad Rouhani; Angel Sappa |
| Title |
Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3066-3072 |
| Keywords |
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| Abstract |
This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided. |
| Address |
San Francisco; CA; USA; June 2010 |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
Call Number  |
ADAS @ adas @ RoS2010a |
Serial |
1303 |
| Permanent link to this record |
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| |
| Author |
Mohammad Rouhani; Angel Sappa |
| Title |
A Fast accurate Implicit Polynomial Fitting Approach |
Type |
Conference Article |
| Year |
2010 |
Publication |
17th IEEE International Conference on Image Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1429–1432 |
| Keywords |
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| Abstract |
This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons. |
| Address |
Hong-Kong |
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Place of Publication |
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Series Issue |
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Edition |
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| ISSN |
1522-4880 |
ISBN |
978-1-4244-7992-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICIP |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RoS2010b |
Serial |
1359 |
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| |
| Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
| Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
Type |
Conference Article |
| Year |
2015 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
231 - 238 |
| Keywords |
Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
| 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. |
| Address |
Hawaii; January 2015 |
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Medium |
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ACDC |
Expedition |
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Conference |
WACV |
| Notes |
ADAS; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ RRG2015 |
Serial |
2499 |
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| |
| Author |
A. Restrepo; Angel Sappa; M. Devy |
| Title |
Edge registration versus triangular mesh registration, a comparative study |
Type |
Journal |
| Year |
2005 |
Publication |
Signal Processing: Image Communication 20: 853–868 (IF: 1.264) |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RSD2005 |
Serial |
567 |
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| Author |
Petia Radeva; A.F. Sole; Antonio Lopez; Joan Serrat |
| Title |
Detecting Nets of Linear Structures in Satellite Images. |
Type |
Miscellaneous |
| Year |
1998 |
Publication |
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Abbreviated Journal |
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Issue |
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| Address |
Londres |
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| Notes |
ADAS;MILAB |
Approved |
no |
| Call Number |
ADAS @ adas @ RSL1998 |
Serial |
25 |
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| Author |
Petia Radeva; A.F. Sole; Antonio Lopez; Joan Serrat |
| Title |
Detecting Nets of Linear Structures in Satellite Images. |
Type |
Miscellaneous |
| Year |
1999 |
Publication |
Machine Vision and Advanced Image Processing in Remote Sensing, Springer, 304–316. |
Abbreviated Journal |
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Issue |
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Pages |
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Edition |
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Medium |
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Conference |
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| Notes |
ADAS;MILAB |
Approved |
no |
| Call Number |
ADAS @ adas @ RSL1999 |
Serial |
34 |
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| |
| Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
| Title |
Multiple-target tracking for the intelligent headlights control |
Type |
Conference Article |
| Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
903–910 |
| Keywords |
Intelligent Headlights |
| Abstract |
TA7.4
Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
| Address |
Madeira Island (Portugal) |
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Edition |
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Medium |
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Expedition |
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Conference |
ITSC |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RSL2010 |
Serial |
1422 |
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| |
| Author |
German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
| Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
Type |
Conference Article |
| Year |
2016 |
Publication |
29th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3234-3243 |
| Keywords |
Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
| Address |
Las Vegas; USA; June 2016 |
| Corporate Author |
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Medium |
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Conference |
CVPR |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ RSM2016 |
Serial |
2739 |
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| |
| Author |
German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez |
| Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
Abbreviated Journal |
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| Volume |
12 |
Issue |
|
Pages |
227-241 |
| Keywords |
SYNTHIA; Virtual worlds; Autonomous Driving |
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. |
| Address |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
Gabriela Csurka |
| Language |
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| Notes |
ADAS; 600.085; 600.082; 600.076; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ RSV2017 |
Serial |
2882 |
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| Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
| Title |
Opponent Colors for Human Detection |
Type |
Conference Article |
| Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
6669 |
Issue |
|
Pages |
363-370 |
| Keywords |
Pedestrian Detection; Color; Part Based Models |
| Abstract |
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
| Address |
Las Palmas de Gran Canaria. Spain |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
| Language |
English |
Summary Language |
English |
Original Title |
Opponent Colors for Human Detection |
| Series Editor |
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Series Title |
Lecture Notes on Computer Science |
Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
Medium |
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| Area |
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Expedition |
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Conference |
IbPRIA |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RVL2011a |
Serial |
1666 |
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| Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
| Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
Type |
Conference Article |
| Year |
2011 |
Publication |
14th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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| Volume |
6855 |
Issue |
II |
Pages |
463-470 |
| Keywords |
Pedestrian Detection; Color |
| Abstract |
Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. |
| Address |
Seville, Spain |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
| Language |
English |
Summary Language |
english |
Original Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
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0302-9743 |
ISBN |
978-3-642-23677-8 |
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Conference |
CAIP |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RVL2011b |
Serial |
1665 |
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| Author |
Angel Sappa; Fadi Dornaika |
| Title |
An Edge-Based Approach to Motion Detection |
Type |
Book Chapter |
| Year |
2006 |
Publication |
6th International Conference on Computational Science (ICCS´06), LNCS 3991:563–570 |
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Pages |
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| Address |
Reading (United Kingdom) |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ SaD2006 |
Serial |
654 |
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| Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
| Title |
Advances in Vision-Based Human Body Modeling |
Type |
Book Chapter |
| Year |
2004 |
Publication |
3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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Pages |
1-26 |
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Editor |
N. Sarris and M. Strintzis. |
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ISBN |
1-59140-299-9 |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ SAG2004a |
Serial |
458 |
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| Author |
Angel Sappa; M.A. Garcia |
| Title |
Hierarchical Clustering of 3D Objects and its Application to Minimum Distance Computation |
Type |
Conference Article |
| Year |
2004 |
Publication |
IEEE International Conference on Robotics & Automation, 5287–5292, New Orleans, LA (USA), ISBN: 0–7803–8232–3 |
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New Orleans, LA, USA |
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ADAS |
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no |
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ADAS @ adas @ SaG2004b |
Serial |
459 |
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| Author |
Angel Sappa; M.A. Garcia |
| Title |
Generating compact representations of static scenes by means of 3D object hierarchies |
Type |
Journal |
| Year |
2007 |
Publication |
The Visual Computer, 23(2): 143–154 |
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ADAS |
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no |
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ADAS @ adas @ SaG2007a |
Serial |
798 |
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