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Author Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez edit   pdf
doi  openurl
  Title Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison Type Journal Article
  Year 2016 Publication Sensors Abbreviated Journal SENS  
  Volume 16 Issue 6 Pages 820  
  Keywords Pedestrian Detection; FIR  
  Abstract Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset.  
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  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1424-8220 ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.085; 600.076; 600.082; 601.281 Approved no  
  Call Number ADAS @ adas @ GFS2016 Serial 2754  
Permanent link to this record
 

 
Author Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras edit   pdf
doi  openurl
  Title Combining Priors, Appearance and Context for Road Detection Type Journal Article
  Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 15 Issue 3 Pages 1168-1178  
  Keywords Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout  
  Abstract Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.076;ISE Approved no  
  Call Number Admin @ si @ ALG2014 Serial 2501  
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Author Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin edit   pdf
url  openurl
  Title Mathematics learning opportunities when playing a Tower Defense Game Type Journal
  Year 2015 Publication International Journal of Serious Games Abbreviated Journal IJSG  
  Volume 2 Issue 4 Pages 57-71  
  Keywords Tower Defense game; learning opportunities; mathematics; problem solving; game design  
  Abstract A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.076;IAM Approved no  
  Call Number Admin @ si @ HJG2015 Serial 2730  
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Author Naveen Onkarappa; Angel Sappa edit  doi
openurl 
  Title Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios Type Journal Article
  Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 15 Issue 1 Pages 136-147  
  Keywords  
  Abstract IF: 3.064
Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields.
 
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.076 Approved no  
  Call Number Admin @ si @ OnS2014a Serial 2386  
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Author Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa edit   pdf
doi  isbn
openurl 
  Title Learning a Part-based Pedestrian Detector in Virtual World Type Journal Article
  Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 15 Issue 5 Pages 2121-2131  
  Keywords Domain Adaptation; Pedestrian Detection; Virtual Worlds  
  Abstract Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1931-0587 ISBN 978-1-4673-2754-1 Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.076 Approved no  
  Call Number ADAS @ adas @ XVL2014 Serial 2433  
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Author Mohammad Rouhani; Angel Sappa; E. Boyer edit  doi
openurl 
  Title Implicit B-Spline Surface Reconstruction Type Journal Article
  Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 24 Issue 1 Pages 22 - 32  
  Keywords  
  Abstract This paper presents a fast and flexible curve, and surface reconstruction technique based on implicit B-spline. This representation does not require any parameterization and it is locally supported. This fact has been exploited in this paper to propose a reconstruction technique through solving a sparse system of equations. This method is further accelerated to reduce the dimension to the active control lattice. Moreover, the surface smoothness and user interaction are allowed for controlling the surface. Finally, a novel weighting technique has been introduced in order to blend small patches and smooth them in the overlapping regions. The whole framework is very fast and efficient and can handle large cloud of points with very low computational cost. The experimental results show the flexibility and accuracy of the proposed algorithm to describe objects with complex topologies. Comparisons with other fitting methods highlight the superiority of the proposed approach in the presence of noise and missing data.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.076 Approved no  
  Call Number Admin @ si @ RSB2015 Serial 2541  
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Author Katerine Diaz; Francesc J. Ferri; W. Diaz edit  doi
openurl 
  Title Incremental Generalized Discriminative Common Vectors for Image Classification Type Journal Article
  Year 2015 Publication IEEE Transactions on Neural Networks and Learning Systems Abbreviated Journal TNNLS  
  Volume 26 Issue 8 Pages 1761 - 1775  
  Keywords  
  Abstract Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2162-237X ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.076 Approved no  
  Call Number Admin @ si @ DFD2015 Serial 2547  
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Author Miguel Oliveira; Angel Sappa; Victor Santos edit  doi
openurl 
  Title A probabilistic approach for color correction in image mosaicking applications Type Journal Article
  Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 14 Issue 2 Pages 508 - 523  
  Keywords Color correction; image mosaicking; color transfer; color palette mapping functions  
  Abstract Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS; 600.076 Approved no  
  Call Number Admin @ si @ OSS2015b Serial 2554  
Permanent link to this record
 

 
Author Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente edit  doi
openurl 
  Title Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy Type Journal Article
  Year 2014 Publication Journal of Biomedical Optics Abbreviated Journal JBiO  
  Volume 19 Issue 12 Pages 126004-1 - 126004-9  
  Keywords  
  Abstract The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
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  Area Expedition Conference  
  Notes (down) ADAS; 600.076 Approved no  
  Call Number Admin @ si @ BLS2014 Serial 2563  
Permanent link to this record
 

 
Author Joan Serrat; Felipe Lumbreras; Antonio Lopez edit   pdf
doi  openurl
  Title Cost estimation of custom hoses from STL files and CAD drawings Type Journal Article
  Year 2013 Publication Computers in Industry Abbreviated Journal COMPUTIND  
  Volume 64 Issue 3 Pages 299-309  
  Keywords On-line quotation; STL format; Regression; Gaussian process  
  Abstract We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%.  
  Address  
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  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
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  Area Expedition Conference  
  Notes (down) ADAS; 600.057; 600.054; 605.203 Approved no  
  Call Number Admin @ si @ SLL2013; ADAS @ adas @ Serial 2161  
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