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David Lloret, Joan Serrat, Antonio Lopez and Juan J. Villanueva. 2003. Ultrasound to MR Volume Registration for Brain Sinking Measurement. 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003.420–427. (LNCS.)
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Daniel Ponsa and Xavier Roca. 2003. Multiple Model Approach to Deformable Shape Tracking. 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003.782–792. (LNCS.)
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2006. Factorization with Missing and Noisy Data. 6th International Conference on Computational Science.555–562.
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2006. An Iterative Multiresolution Scheme for SFM. International Conference on Image Analysis and Recognition.804–815.
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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, 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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Fadi Dornaika and Angel Sappa. 2007. Real-time Vehicle Ego-Motion using Stereo Pairs and Particle Filters. Int. Conf. on Image Analysis and Recognition,.469–480. (LNCS.)
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Angel Sappa, Fadi Dornaika, David Geronimo and Antonio Lopez. 2007. Efficient On-Board Stereo Vision Pose Estimation. Computer Aided Systems Theory, Selected paper from.1183–1190. (LNCS.)
Abstract: This paper presents an efficient technique for real time estimation of on-board stereo vision system pose. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the 3D road points. Fast RANSAC fitting is obtained by selecting points according to a probability distribution function that takes into account the density of points at a given depth. Finally, stereo camera position
and orientation—pose—is computed relative to the road plane. The proposed technique is intended to be used on driver assistance systems for applications such as obstacle or pedestrian detection. A real time performance is reached. Experimental results on several environments and comparisons with a previous work are presented.
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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. Computer Aided Systems Theory,.1105–1112. (LNCS.)
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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