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Author Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca edit   pdf
doi  openurl
  Title Toward Real-Time Pedestrian Detection Based on a Deformable Template Model Type Journal Article
  Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 15 Issue 1 Pages (up) 355-364  
  Keywords  
  Abstract Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed.  
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  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes ISE; 601.213; 600.078 Approved no  
  Call Number PGH2014 Serial 2350  
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Author Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach edit  url
doi  openurl
  Title Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge Type Journal Article
  Year 2022 Publication Gastrointestinal Endoscopy Abbreviated Journal  
  Volume 96 Issue 2 Pages (up) 370-372  
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  Notes ISE Approved no  
  Call Number Admin @ si @ MMB2022 Serial 3701  
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Author Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez edit   pdf
doi  openurl
  Title Selective Spatio-Temporal Interest Points Type Journal Article
  Year 2012 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 116 Issue 3 Pages (up) 396-410  
  Keywords  
  Abstract Recent progress in the field of human action recognition points towards the use of Spatio-TemporalInterestPoints (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1077-3142 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ CHM2012 Serial 1806  
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Author V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich edit  doi
openurl 
  Title Adaptive Correlation Filters for Pattern Recognition Type Journal
  Year 2006 Publication Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 16 Issue 3 Pages (up) 425-431  
  Keywords Pattern recognition, Correlation filters, A adaptive filters  
  Abstract Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ KMA2006a Serial 673  
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Author Marcel P. Lucassen; Theo Gevers; Arjan Gijsenij edit  url
openurl 
  Title Texture Affects Color Emotion Type Journal Article
  Year 2011 Publication Color Research & Applications Abbreviated Journal CRA  
  Volume 36 Issue 6 Pages (up) 426–436  
  Keywords color;texture;color emotion;observer variability;ranking  
  Abstract Several studies have recorded color emotions in subjects viewing uniform color (UC) samples. We conduct an experiment to measure and model how these color emotions change when texture is added to the color samples. Using a computer monitor, our subjects arrange samples along four scales: warm–cool, masculine–feminine, hard–soft, and heavy–light. Three sample types of increasing visual complexity are used: UC, grayscale textures, and color textures (CTs). To assess the intraobserver variability, the experiment is repeated after 1 week. Our results show that texture fully determines the responses on the Hard-Soft scale, and plays a role of decreasing weight for the masculine–feminine, heavy–light, and warm–cool scales. Using some 25,000 observer responses, we derive color emotion functions that predict the group-averaged scale responses from the samples' color and texture parameters. For UC samples, the accuracy of our functions is significantly higher (average R2 = 0.88) than that of previously reported functions applied to our data. The functions derived for CT samples have an accuracy of R2 = 0.80. We conclude that when textured samples are used in color emotion studies, the psychological responses may be strongly affected by texture. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010  
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  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ LGG2011 Serial 1844  
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