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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2007. Motion Segmentation from Feature Trajectories with Missing Data. In J. Marti et al.(Eds.), ed. 3rd. Iberian Conference on Pattern Recognition and Image Analysis.483–490.
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Joan Serrat, Ferran Diego, Jose Manuel Alvarez and Felipe Lumbreras. 2007. Alignment of Videos Recorded from Moving Vehicles. in 14th International Conference on Image Analysis and Processing,.512–517.
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Joan Serrat, Ferran Diego, Felipe Lumbreras and Jose Manuel Alvarez. 2007. Synchronization of Video Sequences from Free-moving Cameras. In J. Marti et al., ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis.620–627. (LNCS.)
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Antonio Lopez, Joan Serrat, Cristina Cañero and Felipe Lumbreras. 2007. Robust Lane Lines Detection and Quantitative Assessment. In J. Marti et al, ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis.274–281. (LNCS.)
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Alex Goldhoorn, Arnau Ramisa, Ramon Lopez de Mantaras and Ricardo Toledo. 2007. Using the Average Landmark Vector Method for Robot Homing. Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA.331–338.
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Arnau Ramisa, Ramon Lopez de Mantaras and Ricardo Toledo. 2007. Comparing Combinations of Feature Regions for Panoramic VSLAM. 4th International Conference on Informatics in Control, Automation and Robotics.292–297.
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Jaume Amores, N. Sebe and Petia Radeva. 2007. Class-Specific Binaryy Correlograms for Object Recognition. British Machine Vision Conference.
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Daniel Ponsa and Antonio Lopez. 2007. Cascade of Classifiers for Vehicle Detection. Advanced Concepts for Intelligent Vision Systems, LNCS 4678, volume 1, pp. 980–989.
Keywords: vehicle detection
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Angel Sappa, Rosa Herrero, Fadi Dornaika, David Geronimo and Antonio Lopez. 2007. Road Approximation in Euclidean and v-Disparity Space: A Comparative Study. EUROCAST2007, Workshop on Cybercars and Intelligent Vehicles.368–369.
Abstract: This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge.
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Jose Manuel Alvarez, Antonio Lopez and Ramon Baldrich. 2007. Shadow Resistant Road Segmentation from a Mobile Monocular System. 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16.
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