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Author X. Orriols; Lluis Barcelo; X. Binefa edit  openurl
  Title Polynomial Fiber Description of Motion for Video Mosaicing, Proceeding ICIP 2001. Type Miscellaneous
  Year 2001 Publication (up) IEEE International Conference on Image Processing, Grecia, 1:1030–1033. Abbreviated Journal  
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  Notes Approved no  
  Call Number Admin @ si @ OBB2001a Serial 143  
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Author Jaime Moreno; Xavier Otazu edit  doi
isbn  openurl
  Title Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder Type Conference Article
  Year 2011 Publication (up) IEEE International Conference on Multimedia and Expo Abbreviated Journal  
  Volume Issue Pages 1-6  
  Keywords  
  Abstract In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder.  
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  ISSN 1945-7871 ISBN 978-1-61284-348-3 Medium  
  Area Expedition Conference ICME  
  Notes CIC Approved no  
  Call Number Admin @ si @ MoO2011a Serial 2176  
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Author Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva edit  doi
isbn  openurl
  Title Visual Summary of Egocentric Photostreams by Representative Keyframes Type Conference Article
  Year 2015 Publication (up) IEEE International Conference on Multimedia and Expo ICMEW2015 Abbreviated Journal  
  Volume Issue Pages 1-6  
  Keywords egocentric; lifelogging; summarization; keyframes  
  Abstract Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries.
 
  Address Torino; italy; July 2015  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue 978-1-4799-7079-7 Edition  
  ISSN ISBN 978-1-4799-7079-7 Medium  
  Area Expedition Conference ICME  
  Notes MILAB Approved no  
  Call Number Admin @ si @ BMT2015 Serial 2638  
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Author Fadi Dornaika; Bogdan Raducanu edit  openurl
  Title Constructing Panoramic Views Through Facial Gaze Tracking Type Conference Article
  Year 2008 Publication (up) IEEE International Conference on Multimedia and Expo, Abbreviated Journal  
  Volume Issue Pages 969–972  
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  Address Hannover (Germany)  
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  Area Expedition Conference ICME  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ DoR2008b Serial 983  
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Author David Guillamet; Jordi Vitria edit  openurl
  Title Determining a Suitable Metric when using Non-negative Matrix Factorization. Type Miscellaneous
  Year 2002 Publication (up) IEEE International Conference on Pattern Recognition, 2: 128–131. Abbreviated Journal  
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  Address Quebec, Canada  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ GVi2002 Serial 294  
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Author J.R. Serra; J.B. Subirana edit  openurl
  Title Perceptual Grouping on Texture Images Using Non-Cartesian Networks Type Journal
  Year 1996 Publication (up) IEEE International Conference on Pattern Recognition. Vol B, pp. 462–466 Abbreviated Journal  
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  Notes Approved no  
  Call Number Admin @ si @ SeS1996a Serial 217  
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Author X. Binefa; Jordi Vitria edit  openurl
  Title A contrast based focusing criterium. Type Miscellaneous
  Year 1996 Publication (up) IEEE International Conference on Pattern Recognition. Vol. A, pp. 805–809 Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BiV1996 Serial 79  
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Author Angel Sappa; M.A. Garcia edit  openurl
  Title Hierarchical Clustering of 3D Objects and its Application to Minimum Distance Computation Type Conference Article
  Year 2004 Publication (up) IEEE International Conference on Robotics & Automation, 5287–5292, New Orleans, LA (USA), ISBN: 0–7803–8232–3 Abbreviated Journal  
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  Address New Orleans, LA, USA  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SaG2004b Serial 459  
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Author Angel Sappa edit  url
openurl 
  Title Efficient Closed Contour Extraction from Range Image Edge Points Type Miscellaneous
  Year 2005 Publication (up) IEEE International Conference on Robotics and Automation Abbreviated Journal  
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  Address Barcelona (Spain)  
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  Notes Approved no  
  Call Number ADAS @ adas @ Sap2005 Serial 538  
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Author Elvina Motard; Bogdan Raducanu; Viviane Cadenat; Jordi Vitria edit  openurl
  Title Incremental On-Line Topological Map Learning for A Visual Homing Application Type Conference Article
  Year 2007 Publication (up) IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 2049–2054  
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  Address Roma (Italy)  
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  Area Expedition Conference ICRA  
  Notes OR; MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MRC2007 Serial 793  
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Author Hugo Berti; Angel Sappa; Osvaldo Agamennoni edit  openurl
  Title Autonomous robot navigation with a global and asymptotic convergence Type Conference Article
  Year 2007 Publication (up) IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 2712–2717  
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  Address Roma (Italy)  
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  Area Expedition Conference ICRA  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ BSA2007 Serial 796  
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Author Bogdan Raducanu; Fadi Dornaika edit  doi
isbn  openurl
  Title Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning Type Conference Article
  Year 2010 Publication (up) IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 156–161  
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  Abstract In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression.  
  Address Anchorage; AK; USA;  
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  ISSN 1050-4729 ISBN 978-1-4244-5038-1 Medium  
  Area Expedition Conference ICRA  
  Notes OR; MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaD2010 Serial 1310  
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Author Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Hierarchical online domain adaptation of deformable part-based models Type Conference Article
  Year 2016 Publication (up) IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 5536-5541  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points.  
  Address Stockholm; Sweden; May 2016  
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  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number Admin @ si @ XVM2016 Serial 2728  
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Author Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy edit   pdf
doi  openurl
  Title End-to-end Driving via Conditional Imitation Learning Type Conference Article
  Year 2018 Publication (up) IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 4693 - 4700  
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  Abstract Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL  
  Address Brisbane; Australia; May 2018  
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  Area Expedition Conference ICRA  
  Notes ADAS; 600.116; 600.124; 600.118 Approved no  
  Call Number Admin @ si @ CML2018 Serial 3108  
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Author Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving Type Conference Article
  Year 2019 Publication (up) IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 2379-2384  
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  Abstract Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet-and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.  
  Address Montreal; Canada; May 2019  
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  Area Expedition Conference ICRA  
  Notes ADAS; 600.124; 600.116; 600.118 Approved no  
  Call Number Admin @ si @ XWY2018 Serial 3182  
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