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Angel Sappa and Fadi Dornaika. 2006. An Edge-Based Approach to Motion Detection. 6th International Conference on Computational Science (ICCS´06), LNCS 3991:563–570.
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Fadi Dornaika and Angel Sappa. 2006. 3D Face Tracking using Appearance Registration and Robust Iterative Closest Point Algorithm. 21st International Symposium on Computer and Information Sciences (ISCIS´06), LNCS 4263: 532–541.
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Fadi Dornaika and Angel Sappa. 2006. Rigid and Non-Rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models. 8th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS´06), LNCS 4179: 675–684.
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Fadi Dornaika and Angel Sappa. 2006. 3D Motion from Image Derivatives using the Least Trimmed Square Regression. International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS´06), LNCS 4153: 76–84.
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Fadi Dornaika and Angel Sappa. 2007. SFM for Planar Scenes: a Direct and Robust Approach. book chapter: Informatics in Control, Automation and Robotics II, Ed. J. Filipe, J. Ferrier, J. Cetto and M. Carvalho, pp. 129–136. (best papers ICINCO 2005).
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Meritxell Vinyals, Arnau Ramisa and Ricardo Toledo. 2007. An Evaluation of an Object Recognition Schema using Multiple Region Detectors. Artificial Intelligence Research and Development, 163:213–222, ISBN: 978–1–58603–798–7, Proceedings of the 10th International Conference of the ACIA (CCIA’07).
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Daniel Ponsa. 2007. Model-Based Visual Localisation of Contours and Vehicles. (Ph.D. thesis, Ediciones Graficas Rey.)
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Angel Sappa, Niki Aifanti, Sotiris Malassiotis and Michael G. Strintzis. 2009. Prior Knowledge Based Motion Model Representation. In Horst Bunke, JuanJose Villanueva and Gemma Sanchez, eds. Progress in Computer Vision and Image Analysis.
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David Geronimo. 2010. A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems. (Ph.D. thesis, Ediciones Graficas Rey.)
Abstract: 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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Jose Manuel Alvarez. 2010. Combining Context and Appearance for Road Detection. (Ph.D. thesis, Ediciones Graficas Rey.)
Abstract: Road traffic crashes have become a major cause of death and injury throughout the world.
Hence, in order to improve road safety, the automobile manufacture is moving towards the
development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit
color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene.
In this way, we present two different approaches to infer the geometry of the road ahead
the moving vehicle. Finally, we focus on combining these context and appearance (color)
approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art.
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