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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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Antonio Lopez and 6 others. 2000. New improvements in the multiscale analysis of trabecular bone patterns. Pattern Recognition and Applications. IOS Press, 251–260.
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Angel Sappa and Boris X. Vintimilla. 2008. Edge Point Linking by Means of Global and Local Schemes. In E. Damiani, ed. in Signal Processing for Image Enhancement and Multimedia Processing. Springer, 115–125.
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Fadi Dornaika and Angel Sappa. 2008. Real Time Image Registration for Planar Structure and 3D Sensor Pose Estimation. In Asim Bhatti, ed. Stereo Vision.299–316.
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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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Niki Aifanti, Angel Sappa, N. Grammalidis and Sotiris Malassiotis. 2009. Advances in Tracking and Recognition of Human Motion. Encyclopedia of Information Science and Technology.65–71.
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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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David Geronimo, Angel Sappa and Antonio Lopez. 2010. Stereo-based Candidate Generation for Pedestrian Protection Systems. Binocular Vision: Development, Depth Perception and Disorders. NOVA Publishers, 189–208.
Abstract: This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided.
Keywords: Pedestrian Detection
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