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Author (down) M. Bressan; Jordi Vitria edit  openurl
  Title Feature Subset Selection in an ICA Space Type Miscellaneous
  Year 2002 Publication Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2002b Serial 277  
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Author (down) M. Bressan; Jordi Vitria edit  openurl
  Title Improving Naive Bayes using Class Conditional ICA Type Miscellaneous
  Year 2002 Publication F. Garijo, J. Riquelme, M. Toro, (Eds.), Advances in Artificial Intelligece–Iberamia, LNAI 2527: 1–10, Springer Verlag Series. Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2002d Serial 278  
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Author (down) M. Bressan; Jordi Vitria edit  openurl
  Title Independent Component Analysis and Naïve Bayes Classification. Type Journal Article
  Year 2002 Publication Proceedings of the Second IASTED International Conference Visualilzation, Imaging and Image Proceesing VIIP 2002: 496–501. Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2002a Serial 288  
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Author (down) M. Bressan; Jordi Vitria edit  openurl
  Title Improving Naive Bayes using Class Condicitonal ICA. Type Miscellaneous
  Year 2002 Publication Iberoamerican Conference on Artificial Intelligence IBERAMIA 2002. Abbreviated Journal  
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  Address Sevilla, Espanya  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2002e Serial 305  
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Author (down) M. Bressan; Jordi Vitria edit  openurl
  Title Independent Feature Selection Type Journal
  Year 2003 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10): 1312–1317 (IF: 3.823) Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2003a Serial 366  
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Author (down) M. Bressan; Jordi Vitria edit  doi
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  Title Nonparametric Discriminant Analysis and Nearest Neighbor Classification Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 24 Issue 15 Pages 2743–2749  
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  Abstract IF: 0.809  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2003b Serial 367  
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Author (down) M. Bressan; David Guillamet; Jordi Vitria edit  openurl
  Title Using a local ICA Representation of High Dimensional Data for Object Recognition and Classification. Type Miscellaneous
  Year 2001 Publication Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Abbreviated Journal  
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  Address Hawaii  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BGV2001 Serial 75  
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Author (down) M. Bressan; David Guillamet; Jordi Vitria edit  openurl
  Title Using an ICA representation of local color histograms for object recognition. Type Miscellaneous
  Year 2000 Publication Butlleti de l´ ACIA, 22:300–307. Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BGV2000 Serial 338  
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Author (down) M. Bressan; David Guillamet; Jordi Vitria edit  openurl
  Title Using an ICA Representation of Local Color Histograms for Object Recognition Type Journal
  Year 2003 Publication Pattern Recognition, 36(3):691–701 (IF: 1.611) Abbreviated Journal  
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  Call Number BCNPCL @ bcnpcl @ BGV2003 Serial 365  
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Author (down) M. Bressan; David Guillamet; Jordi Vitria edit  openurl
  Title Multiclass Object Recognition using Class-Conditional Independent Component Analisis Type Journal
  Year 2004 Publication Cybernetics and Systems, 35/1:35–61 (IF: 0.768) Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BGV2004 Serial 442  
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Author (down) M. Bressan edit  openurl
  Title Un analisis de viabilidad para la confeccion semisupervisada de un mapa de usos del suelo de Catalunya Type Report
  Year 2001 Publication CVC Technical Report #58 Abbreviated Journal  
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  Address CVC (UAB)  
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  Notes Approved no  
  Call Number Admin @ si @ Bre2001 Serial 182  
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Author (down) M. Bressan edit  openurl
  Title Independent modes of variation in Point Distribution models Type Report
  Year 2000 Publication CVC Technical Report #48 Abbreviated Journal  
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  Call Number Admin @ si @ Bre2000 Serial 349  
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Author (down) M. Altillawi; S. Li; S.M. Prakhya; Z. Liu; Joan Serrat edit  doi
openurl 
  Title Implicit Learning of Scene Geometry From Poses for Global Localization Type Journal Article
  Year 2024 Publication IEEE Robotics and Automation Letters Abbreviated Journal ROBOTAUTOMLET  
  Volume 9 Issue 2 Pages 955-962  
  Keywords Localization; Localization and mapping; Deep learning for visual perception; Visual learning  
  Abstract Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets.  
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  ISSN 2377-3766 ISBN Medium  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ Serial 3857  
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Author (down) Luis Herranz; Weiqing Min; Shuqiang Jiang edit  openurl
  Title Food recognition and recipe analysis: integrating visual content, context and external knowledge Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation and restaurant context as emerging directions.  
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  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ HMJ2018 Serial 3250  
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Author (down) Luis Herranz; Shuqiang Jiang; Ruihan Xu edit   pdf
doi  openurl
  Title Modeling Restaurant Context for Food Recognition Type Journal Article
  Year 2017 Publication IEEE Transactions on Multimedia Abbreviated Journal TMM  
  Volume 19 Issue 2 Pages 430 - 440  
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  Abstract Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks.  
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  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ HJX2017 Serial 2965  
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