Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2008). Providing Automatic Multilingual Text Generation to Artificial Cognitive Systems.
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Carlo Gatta, Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2008). Improved Rigid Registration of Vessel Structures using the Fast Radial Symmetry Transform. In Computer Vision for Intravascular Imaging CVII’08 Workshop Medical Image Computing and Computer–Assisted Intervention , 11th International Conference (128–136).
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Carlo Gatta, Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2008). Robust Image-based IVUS Pullbacks Gating. In Proceedings 11th International ConferenceMedical Image Computing and Computer–Assisted Intervention (Vol. 5242, 518–525). LNCS.
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Carme Julia. (2008). Missig Data Matrix Factorization Addressing the Structure from Motion Problem.
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Carme Julia, Angel Sappa, & Felipe Lumbreras. (2008). Aprendiendo a recrear la realidad en 3D. UAB Divulga, Revista de divulgacion cientifica.
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Carme Julia, Angel Sappa, Felipe Lumbreras, & Antonio Lopez. (2008). Recovery of Surface Normals and Reflectance from Different Lighting Conditions. In 5th International Conference on Image Analysis and Recognition (Vol. 5112, 315–325). LNCS.
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Carme Julia, Angel Sappa, Felipe Lumbreras, & Joan Serrat. (2008). Photometric Stereo through and Adapted Alternation Approach. In IEEE International Conference on Image Processing, (1500–1503).
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2008). Rank Estimation in 3D Multibody Motion Segmentation. Electronic Letters, 44(4), 279–280.
Abstract: A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data.
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2008). An Adapted Alternation Approach for Recommender Systems. In IEEE International Conference on e–Business Engineering, (128–135).
Abstract: This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach.
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Carolina Malagelada, Fosca De Iorio, Fernando Azpiroz, Anna Accarino, Santiago Segui, Petia Radeva, et al. (2008). New Insight Into Intestinal Motor Function via Noninvasive Endoluminal Image Analysis. Gastroenterology, 1155–1162.
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Dani Rowe. (2008). Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments.
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David Aldavert, & Ricardo Toledo. (2008). Stereo Vision Local Map Alignment for Robot Environment Mapping. In Robot Vision Second International Workshop, RobVis (Vol. 4931, 111–124). LNCS.
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David Masip, Agata Lapedriza, & Jordi Vitria. (2008). Multitask Learning: An Application to Incremental Face Recognition. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 1, 585–590).
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David Masip, & Jordi Vitria. (2008). Shared Feature Extraction for Nearest Neighbor Face Recognition. IEEE Transactions on Neural Networks, 586–595.
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David Vazquez, & Antonio Lopez. (2008). Intrusion Classification in Intelligent Video Surveillance Systems.
Abstract: An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system
Keywords: Human detection; Car detection; Intrusion detection
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