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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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Joan Marti, Jose Miguel Benedi, Ana Maria Mendonça and Joan Serrat. 2007. Pattern Recognition and Image Analysis. (LNCS.)
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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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David Aldavert and Ricardo Toledo. 2008. Stereo Vision Local Map Alignment for Robot Environment Mapping. Robot Vision Second International Workshop, RobVis.111–124. (LNCS.)
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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, 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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Angel Sappa, ed. 2010. Computer Graphics and Imaging.
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Aura Hernandez-Sabate and Debora Gil. 2012. The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries. In Yasuhiro Honda, ed. Intravascular Ultrasound. Intech, 185–206.
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