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Angel Sappa and Boris X. Vintimilla. 2007. Cost-Based Closed Contour Representations.
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Angel Sappa and M.A. Garcia. 2007. Incremental Integration of Multiresolution Range Images.
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Jaume Amores, N. Sebe and Petia Radeva. 2007. Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms.
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Yu Jie, Jaume Amores, N. Sebe, Petia Radeva and Tian Qi. 2008. Distance Learning for Similarity Estimation.
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Fadi Dornaika and Angel Sappa. 2008. Evaluation of an Appearance-based 3D Face Tracker using Dense 3D Data.
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Hugo Berti, Angel Sappa and Osvaldo Agamennoni. 2008. Improved Dynamic Window Approach by Using Lyapunov Stability Criteria.
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Joan Serrat, Ferran Diego and Felipe Lumbreras. 2008. Los faros delanteros a traves del objetivo.
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Carme Julia, Angel Sappa and Felipe Lumbreras. 2008. Aprendiendo a recrear la realidad en 3D.
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Ferran Diego, Daniel Ponsa, Joan Serrat and Antonio Lopez. 2011. Video Alignment for Change Detection. TIP, 20(7), 1858–1869.
Abstract: In this work, we address the problem of aligning two video sequences. Such alignment refers to synchronization, i.e., the establishment of temporal correspondence between frames of the first and second video, followed by spatial registration of all the temporally corresponding frames. Video synchronization and alignment have been attempted before, but most often in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, restrictive assumptions have been applied, including linear time correspondence or the knowledge of the complete trajectories of corresponding scene points; to some extent, these assumptions limit the practical applicability of any solutions developed. We intend to solve the more general problem of aligning video sequences recorded by independently moving cameras that follow similar trajectories, based only on the fusion of image intensity and GPS information. The novelty of our approach is to pose the synchronization as a MAP inference problem on a Bayesian network including the observations from these two sensor types, which have been proved complementary. Alignment results are presented in the context of videos recorded from vehicles driving along the same track at different times, for different road types. In addition, we explore two applications of the proposed video alignment method, both based on change detection between aligned videos. One is the detection of vehicles, which could be of use in ADAS. The other is online difference spotting videos of surveillance rounds.
Keywords: video alignment
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Fadi Dornaika and Angel Sappa. 2009. A Featureless and Stochastic Approach to On-board Stereo Vision System Pose. IMAVIS, 27(9), 1382–1393.
Abstract: This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach.
Keywords: On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping
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