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Author Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez edit   pdf
url  openurl
  Title GPU-based pedestrian detection for autonomous driving Type Conference Article
  Year 2016 Publication 16th International Conference on Computational Science Abbreviated Journal  
  Volume 80 Issue Pages 2377-2381  
  Keywords Pedestrian detection; Autonomous Driving; CUDA  
  Abstract We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.  
  Address (down) San Diego; CA; USA; June 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICCS  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number ADAS @ adas @ CSE2016 Serial 2741  
Permanent link to this record
 
 
Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat edit  openurl
  Title Photometric Stereo through and Adapted Alternation Approach Type Conference Article
  Year 2008 Publication IEEE International Conference on Image Processing, Abbreviated Journal  
  Volume Issue Pages 1500–1503  
  Keywords  
  Abstract  
  Address (down) San Diego; CA; USA  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2008d Serial 1016  
Permanent link to this record
 
 
Author Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Geographic Information for vision-based Road Detection Type Conference Article
  Year 2010 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal  
  Volume Issue Pages 621–626  
  Keywords road detection  
  Abstract Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.  
  Address (down) San Diego; CA; USA  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference IV  
  Notes ADAS;ISE Approved no  
  Call Number ADAS @ adas @ ALG2010 Serial 1428  
Permanent link to this record
 
 
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 IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 2712–2717  
  Keywords  
  Abstract  
  Address (down) Roma (Italy)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICRA  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ BSA2007 Serial 796  
Permanent link to this record
 
 
Author Diego Cheda; Daniel Ponsa; Antonio Lopez edit   pdf
url  openurl
  Title Monocular Depth-based Background Estimation Type Conference Article
  Year 2012 Publication 7th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume Issue Pages 323-328  
  Keywords  
  Abstract In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences.  
  Address (down) Roma  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference VISAPP  
  Notes ADAS Approved no  
  Call Number Admin @ si @ CPL2012b; ADAS @ adas @ cpl2012e Serial 2012  
Permanent link to this record
 
 
Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
url  openurl
  Title Factorization with Missing and Noisy Data Type Conference Article
  Year 2006 Publication 6th International Conference on Computational Science Abbreviated Journal ICCS´06  
  Volume LNCS 3991 Issue Pages 555–562  
  Keywords  
  Abstract  
  Address (down) Reading (United Kingdom)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2006b Serial 653  
Permanent link to this record
 
 
Author Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo edit  url
doi  openurl
  Title LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations Type Conference Article
  Year 2015 Publication 22th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 178 - 181  
  Keywords  
  Abstract  
  Address (down) Quebec; Canada; September 2015  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICIP  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ AST2015 Serial 2630  
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez edit   pdf
url  doi
isbn  openurl
  Title Color Attributes for Object Detection Type Conference Article
  Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3306-3313  
  Keywords pedestrian detection  
  Abstract State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape.
In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe-
art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.		  
  Address (down) Providence; Rhode Island; USA;  
  Corporate Author Thesis  
  Publisher IEEE Xplore Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS; CIC; Approved no  
  Call Number Admin @ si @ KRW2012 Serial 1935  
Permanent link to this record
 
 
Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Unsupervised co-segmentation through region matching Type Conference Article
  Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 749-756  
  Keywords  
  Abstract Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database.  
  Address (down) Providence, Rhode Island  
  Corporate Author Thesis  
  Publisher IEEE Xplore Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RSL2012b; ADAS @ adas @ Serial 2033  
Permanent link to this record
 
 
Author Chris Bahnsen; David Vazquez; Antonio Lopez; Thomas B. Moeslund edit  url
doi  openurl
  Title Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data Type Conference Article
  Year 2019 Publication 14th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume Issue Pages 123-130  
  Keywords Rain Removal; Traffic Surveillance; Image Denoising  
  Abstract Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance.  
  Address (down) Praga; Czech Republic; February 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference VISIGRAPP  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ BVL2019 Serial 3256  
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