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Author |
Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat |
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Title |
Pattern Recognition and Image Analysis |
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2007 |
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3rd Iberian Conference |
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6669 |
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4477-4478 |
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Girona (Spain) |
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IbPRIA |
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ADAS |
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no |
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ADAS @ adas @ MBM2007 |
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994 |
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Author |
Mario Hernandez; Joao Sanchez; Jordi Vitria |
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Title |
Selected papers from Iberian Conference on Pattern Recognition and Image Analysis |
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2012 |
Publication |
Pattern Recognition |
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45 |
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9 |
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3047-3582 |
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0031-3203 |
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OR;MV |
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no |
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Admin @ si @ HSV2012 |
Serial |
2069 |
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Author |
Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez |
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Title |
Computer Vision in Vehicle Technology: Land, Sea & Air |
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Book Whole |
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Year |
2017 |
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161-163 |
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Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition. |
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John Wiley & Sons, Ltd |
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978-1-118-86807-2 |
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ADAS; 600.118 |
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Admin @ si @ LIP2017a |
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2937 |
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Author |
Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Title |
Traffic-Sign Recognition Systems |
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2011 |
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SpringerBriefs in Computer Science |
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5-13 |
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Springer London |
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978-1-4471-2244-9 |
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MILAB; OR;HuPBA;MV |
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no |
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Admin @ si @ EBP2011 |
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1801 |
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Author |
David Geronimo; Antonio Lopez |
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Title |
Vision-based Pedestrian Protection Systems for Intelligent Vehicles |
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2014 |
Publication |
SpringerBriefs in Computer Science |
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Pages |
1-114 |
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Keywords |
Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users |
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Abstract |
Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented. |
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Springer Briefs in Computer Vision |
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978-1-4614-7986-4 |
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ADAS; 600.076 |
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no |
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GeL2014 |
Serial |
2325 |
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Author |
David Vazquez |
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Title |
Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
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Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat de Barcelona-CVC |
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Volume |
1 |
Issue |
1 |
Pages |
1-105 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
Pedestrian detection is of paramount interest for many applications, e.g. Advanced Driver Assistance Systems, Intelligent Video Surveillance and Multimedia systems. Most promising pedestrian detectors rely on appearance-based classifiers trained with annotated data. However, the required annotation step represents an intensive and subjective task for humans, what makes worth to minimize their intervention in this process by using computational tools like realistic virtual worlds. The reason to use these kind of tools relies in the fact that they allow the automatic generation of precise and rich annotations of visual information. Nevertheless, the use of this kind of data comes with the following question: can a pedestrian appearance model learnt with virtual-world data work successfully for pedestrian detection in real-world scenarios?. To answer this question, we conduct different experiments that suggest a positive answer. However, the pedestrian classifiers trained with virtual-world data can suffer the so called dataset shift problem as real-world based classifiers does. Accordingly, we have designed different domain adaptation techniques to face this problem, all of them integrated in a same framework (V-AYLA). We have explored different methods to train a domain adapted pedestrian classifiers by collecting a few pedestrian samples from the target domain (real world) and combining them with many samples of the source domain (virtual world). The extensive experiments we present show that pedestrian detectors developed within the V-AYLA framework do achieve domain adaptation. Ideally, we would like to adapt our system without any human intervention. Therefore, as a first proof of concept we also propose an unsupervised domain adaptation technique that avoids human intervention during the adaptation process. To the best of our knowledge, this Thesis work is the first demonstrating adaptation of virtual and real worlds for developing an object detector. Last but not least, we also assessed a different strategy to avoid the dataset shift that consists in collecting real-world samples and retrain with them in such a way that no bounding boxes of real-world pedestrians have to be provided. We show that the generated classifier is competitive with respect to the counterpart trained with samples collected by manually annotating pedestrian bounding boxes. The results presented on this Thesis not only end with a proposal for adapting a virtual-world pedestrian detector to the real world, but also it goes further by pointing out a new methodology that would allow the system to adapt to different situations, which we hope will provide the foundations for future research in this unexplored area. |
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Address |
Barcelona |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
Barcelona |
Editor |
Antonio Lopez;Daniel Ponsa |
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Language |
English |
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978-84-940530-1-6 |
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adas |
Approved |
yes |
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Call Number |
ADAS @ adas @ Vaz2013 |
Serial |
2276 |
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Author |
Laura Igual; Santiago Segui |
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Title |
Introduction to Data Science – A Python Approach to Concepts, Techniques and Applications. Undergraduate Topics in Computer Science |
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Book Whole |
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2017 |
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Abbreviated Journal |
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1-215 |
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978-3-319-50016-4 |
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978-3-319-50016-4 |
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MILAB |
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no |
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Call Number |
Admin @ si @ IgS2017 |
Serial |
3027 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Computer Vision in the Infrared Spectrum: Challenges and Approaches |
Type |
Book Whole |
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Year |
2021 |
Publication |
Synthesis Lectures on Computer Vision |
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10 |
Issue |
2 |
Pages |
1-138 |
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Abstract |
Human visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can provably increase the robustness of many autonomous systems. Applications that can benefit from utilizing infrared imagery range from robotics to automotive and from biometrics to surveillance. In this book, we provide a brief yet concise introduction to the current state-of-the-art of computer vision and machine learning in the infrared spectrum. Based on various popular computer vision tasks such as image enhancement, object detection, or object tracking, we first motivate each task starting from established literature in the visual-optical spectrum. Then, we discuss the differences between processing images and videos in the visual-optical spectrum and the various infrared spectra. An overview of the current literature is provided together with an outlook for each task. Furthermore, available and annotated public datasets and common evaluation methods and metrics are presented. In a separate chapter, popular applications that can greatly benefit from the use of infrared imagery as a data source are presented and discussed. Among them are automatic target recognition, video surveillance, or biometrics including face recognition. Finally, we conclude with recommendations for well-fitting sensor setups and data processing algorithms for certain computer vision tasks. We address this book to prospective researchers and engineers new to the field but also to anyone who wants to get introduced to the challenges and the approaches of computer vision using infrared images or videos. Readers will be able to start their work directly after reading the book supported by a highly comprehensive backlog of recent and relevant literature as well as related infrared datasets including existing evaluation frameworks. Together with consistently decreasing costs for infrared cameras, new fields of application appear and make computer vision in the infrared spectrum a great opportunity to face nowadays scientific and engineering challenges. |
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978-1636392431 |
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MSIAU |
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Admin @ si @ TSH2021 |
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3666 |
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Author |
A. Sanfeliu; Juan J. Villanueva; Jordi Vitria |
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Title |
Image Analysis and Pattern Recognition. |
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1997 |
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Image Analysis and Pattern Recognition. |
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OR;MV |
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BCNPCL @ bcnpcl @ SVV1997 |
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56 |
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Author |
Ramon Baldrich |
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Title |
Perceptual approach to a computational colour-texture representation for surface inspection. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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CIC |
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no |
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CAT @ cat @ Bal2001 |
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73 |
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Author |
Ricardo Toledo |
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Title |
Cardiac workstation and dynamic model to assist in coronary tree analysis. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Petia Radeva;JuanJose Villanueva |
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Admin @ si @ Tol2001 |
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166 |
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Author |
Antonio Lopez |
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Title |
Multilocal Methods for Ridge and Valley Delineation in Image Analysis. |
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2000 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Joan Serrat |
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ADAS @ adas @ Lop2000 |
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174 |
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Author |
Felipe Lumbreras |
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Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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ADAS |
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no |
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ADAS @ adas @ Lum2001 |
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188 |
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Author |
A. Pujol |
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Title |
Contributions to shape and texture face similarity measurement. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Editor |
JuanJose Villanueva |
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no |
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Admin @ si @ Puj2001 |
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202 |
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Author |
Javier Varona |
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Title |
Seguimiento visual robusto en entornos complejos |
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2001 |
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PhD Thesis |
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no |
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Admin @ si @ Var2001 |
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214 |
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