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Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
![download PDF file pdf](img/file_PDF.gif)
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Title |
LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
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Conference Article |
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Year |
2016 |
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14th European Conference on Computer Vision Workshops |
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Volume |
9915 |
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Pages |
894-900 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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ADAS;IAM; 600.085; 600.076 |
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MHE2016 |
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2865 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Error-tolerant coarse-to-fine matching model for hierarchical graphs |
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Conference Article |
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Year |
2017 |
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11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition |
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10310 |
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107-117 |
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Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching |
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Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Anacapri; Italy; May 2017 |
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Springer International Publishing |
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Pasquale Foggia; Cheng-Lin Liu; Mario Vento |
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GbRPR |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ RLF2017a |
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2951 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation |
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Conference Article |
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Year |
2021 |
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28th IEEE International Conference on Image Processing |
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19-22 |
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This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Anchorage-Alaska; USA; September 2021 |
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ICIP |
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MSIAU; 600.130; 600.122; 601.349 |
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Admin @ si @ SSV2021b |
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3579 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning |
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Conference Article |
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2010 |
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IEEE International Conference on Robotics and Automation |
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156–161 |
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In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression. |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Anchorage; AK; USA; |
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1050-4729 |
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978-1-4244-5038-1 |
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ICRA |
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OR; MV |
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BCNPCL @ bcnpcl @ RaD2010 |
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1310 |
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Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Julio C. S. Jacques Junior; Xavier Baro; Evelyne Viegas; Yagmur Gucluturk; Umut Guclu; Marcel A. J. van Gerven; Rob van Lier; Meysam Madadi; Stephane Ayache |
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Title |
Design of an Explainable Machine Learning Challenge for Video Interviews |
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Conference Article |
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2017 |
Publication |
International Joint Conference on Neural Networks |
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This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration. |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Anchorage; Alaska; USA; May 2017 |
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IJCNN |
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HUPBA; no proj |
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no |
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Admin @ si @ EGE2017 |
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2922 |
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Sergio Escalera; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon |
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Title |
ChaLearn Looking at People: A Review of Events and Resources |
Type |
Conference Article |
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2017 |
Publication |
30th International Joint Conference on Neural Networks |
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This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities. |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Anchorage; Alaska; USA; May 2017 |
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IJCNN |
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Notes |
HuPBA; 602.143 |
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no |
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Admin @ si @ EBE2017 |
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3012 |
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Author |
Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
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Conference Article |
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2007 |
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4th International Conference on Informatics in Control, Automation and Robotics |
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292–297 |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Angers (France) |
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ICINCO |
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RV;ADAS |
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no |
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Admin @ si @ RLA2007 |
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900 |
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Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti |
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Title |
A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa |
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2003 |
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Proceedings of Pattern Recognition in Information Systems |
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1-13 |
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Angers, France |
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ICEIS Press |
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972-98816-3-4 |
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PRIS'03 |
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DAG;IAM; |
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IAM @ iam @ LVS2003 |
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1576 |
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Author |
Francisco Jose Perales; Juan J. Villanueva; Yuhua Luo |
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Title |
Matching Criteria |
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Conference Article |
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Year |
1991 |
Publication |
Computer and Information Sciences VI, Proceedings of the 1991 International Symposium on Computer and Information Sciences |
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1 |
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1029-1038 |
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Antalya, Turkey, 30 October-2 November 1991 |
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Elsevier Science Pub. |
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0097-8493 |
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ISE @ ise @ PVL1991a |
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264 |
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Author |
David Geronimo |
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Title |
A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems |
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2010 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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At the beginning of the 21th century, traffic accidents have become a major problem not only for developed countries but also for emerging ones. As in other scientific areas in which Artificial Intelligence is becoming a key actor, advanced driver assistance systems, and concretely pedestrian protection systems based on Computer Vision, are becoming a strong topic of research aimed at improving the safety of pedestrians. However, the challenge is of considerable complexity due to the varying appearance of humans (e.g., clothes, size, aspect ratio, shape, etc.), the dynamic nature of on-board systems and the unstructured moving environments that urban scenarios represent. In addition, the required performance is demanding both in terms of computational time and detection rates. In this thesis, instead of focusing on improving specific tasks as it is frequent in the literature, we present a global approach to the problem. Such a global overview starts by the proposal of a generic architecture to be used as a framework both to review the literature and to organize the studied techniques along the thesis. We then focus the research on tasks such as foreground segmentation, object classification and refinement following a general viewpoint and exploring aspects that are not usually analyzed. In order to perform the experiments, we also present a novel pedestrian dataset that consists of three subsets, each one addressed to the evaluation of a different specific task in the system. The results presented in this thesis not only end with a proposal of a pedestrian detection system but also go one step beyond by pointing out new insights, formalizing existing and proposed algorithms, introducing new techniques and evaluating their performance, which we hope will provide new foundations for future research in the area. |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras |
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978-84-936529-5-1 |
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ADAS |
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no |
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ADAS @ adas @ Ger2010 |
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1279 |
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Author |
Maria Alberich-Carramiñana; Guillem Alenya; Juan Andrade; E. Martinez; Carme Torras |
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Title |
Affine Epipolar Direction from Two Views of a Planar Contour |
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Book Chapter |
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2006 |
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Proceedings of the Advanced Concepts for Intelligent Vision Systems Conference, LNCS 4179: 944–955 |
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Antwerp (Belgium) |
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Admin @ si @ AAA2006 |
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661 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Rigid and Non-Rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models |
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2006 |
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8th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS´06), LNCS 4179: 675–684 |
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Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
Antwerp (Belgium) |
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ADAS |
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no |
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ADAS @ adas @ DoS2006c |
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689 |
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Permanent link to this record |
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Author |
Jiaolong Xu |
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Title |
Domain Adaptation of Deformable Part-based Models |
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Book Whole |
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Year |
2015 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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On-board pedestrian detection is crucial for Advanced Driver Assistance Systems
(ADAS). An accurate classication is fundamental for vision-based pedestrian detection.
The underlying assumption for learning classiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classiers. However, in practice, there are dierent reasons that can break this constancy assumption. Accordingly, reusing existing classiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset.
We start by exploiting the maximum detection accuracy of the virtual-world
trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still suer from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require
revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area. |
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Antonio Lopez |
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978-84-943427-1-4 |
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ADAS; 600.076 |
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Admin @ si @ Xu2015 |
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2631 |
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Author |
Cesar de Souza |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video |
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2018 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
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April 2018 |
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Ediciones Graficas Rey |
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Antonio Lopez;Naila Murray |
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ADAS; 600.118 |
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Admin @ si @ Sou2018 |
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3127 |
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Author |
Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Towards Visual Personality Questionnaires based on Deep Learning and Social Media |
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2019 |
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21st International Conference on Social Influence and Social Psychology |
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April 2019; Tokio; Japan |
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ICSISP |
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ISE; 600.119 |
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Admin @ si @ RGG2020 |
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3554 |
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