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Antonio Lopez, Atsushi Imiya, Tomas Pajdla and Jose Manuel Alvarez. 2017. Computer Vision in Vehicle Technology: Land, Sea & Air. John Wiley & Sons, Ltd.
Abstract: 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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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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Angel Sappa, Niki Aifanti, N. Grammalidis and Sotiris Malassiotis. 2004. Advances in Vision-Based Human Body Modeling. In N. Sarris and M. Strintzis., ed. 3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body.1–26.
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Joan Marti, Jose Miguel Benedi, Ana Maria Mendonça and Joan Serrat. 2007. Pattern Recognition and Image Analysis. (LNCS.)
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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. 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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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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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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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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Jose Manuel Alvarez and Antonio Lopez. 2012. Photometric Invariance by Machine Learning. In Theo Gevers, A.G., Joost van de Weijer, Jan-Mark Geusebroek, ed. Color in Computer Vision: Fundamentals and Applications. iConcept Press Ltd, 113–134.
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