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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, 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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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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Angel Sappa, ed. 2010. Computer Graphics and Imaging.
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