| Records |
| Author |
A. Pujol; Felipe Lumbreras; X. Varona; Juan J. Villanueva |
| Title |
Locating people in indoor scenes for real applications. |
Type |
Conference Article |
| Year |
2000 |
Publication |
15 th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
4 |
Issue |
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Pages |
632-635 |
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| Address |
Barcelona. |
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Conference |
ICPR |
| Notes |
ADAS |
Approved |
no |
Call Number  |
ADAS @ adas @ PLV2000 |
Serial |
237 |
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| Author |
A. Pujol; Felipe Lumbreras; X. Varona; Juan J. Villanueva |
| Title |
Template matching through invariant eigenspace projection. |
Type |
Miscellaneous |
| Year |
1999 |
Publication |
Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Bilbao |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ PLV1999 |
Serial |
6 |
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| Author |
Daniel Ponsa; Antonio Lopez; Joan Serrat; Felipe Lumbreras; T. Graf |
| Title |
Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1108–1113, ISBN:0–7803–9216–7 |
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Issue |
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Pages |
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| Keywords |
vehicle detection |
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| Address |
Vienna (Austria) |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ PLS2005 |
Serial |
615 |
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| Author |
Daniel Ponsa; Antonio Lopez; Felipe Lumbreras; Joan Serrat; T. Graf |
| Title |
3D Vehicle Sensor based on Monocular Vision |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1096–1101, ISBN:0–7803–9216–7 |
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Vienna (Austria) |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ PLL2005 |
Serial |
614 |
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| Author |
A. Pujol; Antonio Lopez; Jose Luis Alba; Juan J. Villanueva |
| Title |
Ridges, Valleys and Hausdorff Based Similarity Measures for Face Detection and Matching |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceedings of the 1st International Workshop on Pattern Recognition in Information Systems (PRIS’2001), ICEIS Press, Ana Fred and Anil K. Jain (Eds), pgs.80–90 |
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| Address |
Setubal (Portugal) |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ PLA2001 |
Serial |
486 |
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| Author |
Naveen Onkarappa; Angel Sappa |
| Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
Type |
Conference Article |
| Year |
2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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| Volume |
6111 |
Issue |
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Pages |
230-239 |
| Keywords |
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| Abstract |
This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
| Address |
Povoa de Varzim (Portugal) |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-13771-6 |
Medium |
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Expedition |
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Conference |
ICIAR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ OnS2010 |
Serial |
1342 |
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| Author |
R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
| Title |
On-line Semantic Perception Using Uncertainty |
Type |
Conference Article |
| Year |
2012 |
Publication |
International Conference on Intelligent Robots and Systems |
Abbreviated Journal |
IROS |
| Volume |
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Issue |
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Pages |
4185-4191 |
| Keywords |
Semantic Segmentation |
| Abstract |
Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance |
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Conference |
IROS |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ NRR2012 |
Serial |
2378 |
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| Author |
W. Niessen; Antonio Lopez; W. Van Enk; P. Van Roermund; Bart M. Ter Haar Romeny; M. Viergever |
| Title |
In Vivo Analysis of Trabecular Bone Architecture. |
Type |
Miscellaneous |
| Year |
1997 |
Publication |
Information Processing in Medical Imaging, pp. 435–440. |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ NLE1997b |
Serial |
67 |
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| Author |
W. Niessen; Antonio Lopez; W. Van Enk; P. Van Roermund; Bart M. Ter Haar Romeny; M. Viergever |
| Title |
Multiscale Trabecular Bone Orientation Analysis. |
Type |
Miscellaneous |
| Year |
1997 |
Publication |
7th Spanish National Symposium on Pattern Recognition and Image Analysis, pp. 19–24. |
Abbreviated Journal |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ NLE1997a |
Serial |
66 |
| Permanent link to this record |
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| |
| Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva |
| Title |
Occlusion handling via random subspace classifiers for human detection |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Systems, Man, and Cybernetics (Part B) |
Abbreviated Journal |
TSMCB |
| Volume |
44 |
Issue |
3 |
Pages |
342-354 |
| Keywords |
Pedestriand Detection; occlusion handling |
| Abstract |
This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes |
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Edition |
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| ISSN |
2168-2267 |
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| Notes |
ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ MVL2014 |
Serial |
2213 |
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| Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe |
| Title |
Random Forests of Local Experts for Pedestrian Detection |
Type |
Conference Article |
| Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2592 - 2599 |
| Keywords |
ADAS; Random Forest; Pedestrian Detection |
| Abstract |
Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. |
| Address |
Sydney; Australia; December 2013 |
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| Publisher |
IEEE |
Place of Publication |
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Edition |
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| ISSN |
1550-5499 |
ISBN |
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Medium |
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Expedition |
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Conference |
ICCV |
| Notes |
ADAS; 600.057; 600.054 |
Approved |
no |
| Call Number |
ADAS @ adas @ MVL2013 |
Serial |
2333 |
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| Author |
Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez |
| Title |
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest |
Type |
Miscellaneous |
| Year |
2016 |
Publication |
Arxiv |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
Domain Adaptation; Pedestrian detection; Random Forest |
| Abstract |
Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ MVJ2016 |
Serial |
2868 |
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| Author |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez |
| Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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 |
137–144 |
| Keywords |
Pedestrian Detection; Domain Adaptation |
| Abstract |
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance. |
| Address |
San Francisco; CA; USA; June 2010 |
| Corporate Author |
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Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
| Series Editor |
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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 @ MVG2010 |
Serial |
1304 |
| Permanent link to this record |
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| Author |
Javier Marin; David Geronimo; David Vazquez; Antonio Lopez |
| Title |
Pedestrian Detection: Exploring Virtual Worlds |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Handbook of Pattern Recognition: Methods and Application |
Abbreviated Journal |
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| Volume |
5 |
Issue |
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Pages |
145-162 |
| Keywords |
Virtual worlds; Pedestrian Detection; Domain Adaptation |
| Abstract |
Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. |
| Address |
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Thesis |
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| Publisher |
iConcept Press |
Place of Publication |
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Editor |
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| Language |
English |
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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ISBN |
978-1-477554-82-1 |
Medium |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ MGV2012 |
Serial |
1979 |
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| Author |
Judit Martinez; Eva Costa; P. Herreros; Antonio Lopez; Juan J. Villanueva |
| Title |
TV-Screen Quality Inspection by Artificial Vision |
Type |
Conference Article |
| Year |
2003 |
Publication |
Proceedings SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision (QCAV 2003) |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
A real-time vision system for TV screen quality inspection is introduced. The whole system consists of eight cameras and one processor per camera. It acquires and processes 112 images in 6 seconds. The defects to be inspected can be grouped into four main categories (bubble, line-out, line reduction and landing) although there exists a large variability among each particular type of defect. The complexity of the whole inspection process has been reduced by dividing images into smaller ones and grouping the defects into frequency and intensity relevant ones. Tools such as mathematical morphology, Fourier transform, profile analysis and classification have been used. The performance of the system has been successfully proved against human operators in normal production conditions. |
| Address |
Gatlinburg, (EEUU) |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ MCH2003a |
Serial |
393 |
| Permanent link to this record |
