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Diego Cheda, Daniel Ponsa and Antonio Lopez. 2012. Monocular Egomotion Estimation based on Image Matching. 1st International Conference on Pattern Recognition Applications and Methods.425–430.
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Jose Carlos Rubio, Joan Serrat and Antonio Lopez. 2012. Multiple target tracking and identity linking under split, merge and occlusion of targets and observations. 1st International Conference on Pattern Recognition Applications and Methods.
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Ferran Diego, G.D. Evangelidis and Joan Serrat. 2012. Night-time outdoor surveillance by mobile cameras. 1st International Conference on Pattern Recognition Applications and Methods.365–371.
Abstract: This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods.
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X. Orriols, Ricardo Toledo, X. Binefa, Petia Radeva, Jordi Vitria and Juan J. Villanueva. 2000. Probabilistic Saliency Approach for Elongated Structure Detection using Deformable Models. 15 th International Conference on Pattern Recognition.1006–1009.
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David Lloret, Joan Serrat, Antonio Lopez, A. Soler and Juan J. Villanueva. 2000. Retinal image registration using creases as anatomical landmarks. 15 th International Conference on Pattern Recognition.207–2010.
Abstract: Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized
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Ricardo Toledo and 6 others. 2000. Eigensnakes for vessel segmentation in angiography. 15 th International Conference on Pattern Recognition.340–343.
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A. Pujol, Felipe Lumbreras, Javier Varona and Juan J. Villanueva. 2000. Locating people in indoor scenes for real applications. 15 th International Conference on Pattern Recognition.632–635.
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Cristina Cañero, Petia Radeva, Ricardo Toledo, Juan J. Villanueva and J. Mauri. 2000. 3D Curve Reconstruction by Biplane Snakes. 15 th International Conference on Pattern Recognition.563–566.
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Joan Serrat, Antonio Lopez and David Lloret. 2000. On ridges and valleys. 15 th International Conference on Pattern Recognition.59–66.
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Jaume Amores. 2010. Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study. 20th International Conference on Pattern Recognition.4246–4250.
Abstract: Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance.
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