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Author Diego Cheda; Daniel Ponsa; Antonio Lopez edit   pdf
openurl 
  Title Monocular Egomotion Estimation based on Image Matching Type Conference Article
  Year 2012 Publication 1st International Conference on Pattern Recognition Applications and Methods Abbreviated Journal  
  Volume Issue Pages 425-430  
  Keywords (up) SLAM  
  Abstract  
  Address Portugal  
  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 ICPRAM  
  Notes ADAS Approved no  
  Call Number Admin @ si @ CPL2012a;; ADAS @ adas @ Serial 2011  
Permanent link to this record
 

 
Author German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez edit   pdf
openurl 
  Title Visual SLAM for Driverless Cars: A Brief Survey Type Conference Article
  Year 2012 Publication IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles Abbreviated Journal  
  Volume Issue Pages  
  Keywords (up) SLAM  
  Abstract  
  Address Alcalá de Henares  
  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 IVW  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RSP2012; ADAS @ adas Serial 2019  
Permanent link to this record
 

 
Author German Ros; J. Guerrero; Angel Sappa; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title VSLAM pose initialization via Lie groups and Lie algebras optimization Type Conference Article
  Year 2013 Publication Proceedings of IEEE International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages 5740 - 5747  
  Keywords (up) SLAM  
  Abstract We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm.  
  Address Karlsruhe; Germany; May 2013  
  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 1050-4729 ISBN 978-1-4673-5641-1 Medium  
  Area Expedition Conference ICRA  
  Notes ADAS; 600.054; 600.055; 600.057 Approved no  
  Call Number Admin @ si @ RGS2013a; ADAS @ adas @ Serial 2225  
Permanent link to this record
 

 
Author Petia Radeva; Joan Serrat; Enric Marti edit   pdf
doi  openurl
  Title A snake for model-based segmentation Type Conference Article
  Year 1995 Publication Proc. Conf. Fifth Int Computer Vision Abbreviated Journal  
  Volume Issue Pages 816-821  
  Keywords (up) snakes; elastic matching; model-based segmenta tion  
  Abstract Despite the promising results of numerous applications, the hitherto proposed snake techniques share some common problems: snake attraction by spurious edge points, snake degeneration (shrinking and attening), convergence and stability of the deformation process, snake initialization and local determination of the parameters of elasticity. We argue here that these problems can be solved only when all the snake aspects are considered. The snakes proposed here implement a new potential eld and external force in order to provide a deformation convergence, attraction by both near and far edges as well as snake behaviour selective according to the edge orientation. Furthermore, we conclude that in the case of model-based seg mentation, the internal force should include structural information about the expected snake shape. Experiments using this kind of snakes for segmenting bones in complex hand radiographs show a signi cant improvement.  
  Address  
  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 MILAB;ADAS;IAM Approved no  
  Call Number IAM @ iam @ RSM1995 Serial 1634  
Permanent link to this record
 

 
Author Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores edit   pdf
doi  openurl
  Title Spatiotemporal Stacked Sequential Learning for Pedestrian Detection Type Conference Article
  Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal  
  Volume Issue Pages 3-12  
  Keywords (up) SSL; Pedestrian Detection  
  Abstract Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera.  
  Address Santiago de Compostela; España; June 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 ACDC Expedition Conference IbPRIA  
  Notes ADAS; 600.057; 600.054; 600.076 Approved no  
  Call Number GRV2015; ADAS @ adas @ GRV2015 Serial 2454  
Permanent link to this record
 

 
Author Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez edit   pdf
openurl 
  Title Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching Type Conference Article
  Year 2016 Publication GPU Technology Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords (up) Stereo; Autonomous Driving; GPU; 3d reconstruction  
  Abstract Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms  
  Address Silicon Valley; San Francisco; USA; April 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 GTC  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number ADAS @ adas @ HME2016 Serial 2738  
Permanent link to this record
 

 
Author Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez edit   pdf
openurl 
  Title Obstacle mapping module for quadrotors on outdoor Search and Rescue operations Type Conference Article
  Year 2013 Publication International Micro Air Vehicle Conference and Flight Competition Abbreviated Journal  
  Volume Issue Pages  
  Keywords (up) UAV  
  Abstract Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments.
 
  Address Toulouse; France; September 2013  
  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 IMAV  
  Notes ADAS; 600.054; 600.057;IAM Approved no  
  Call Number Admin @ si @ NSH2013 Serial 2371  
Permanent link to this record
 

 
Author Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie edit   pdf
url  openurl
  Title Inferring the Performance of Medical Imaging Algorithms Type Conference Article
  Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6854 Issue Pages 520-528  
  Keywords (up) Validation, Statistical Inference, Medical Imaging Algorithms.  
  Abstract Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
 
  Address Sevilla  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Heidelberg Place of Publication Berlin Editor Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch  
  Language Summary Language Original Title  
  Series Editor Series Title L Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CAIP  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HGR2011 Serial 1676  
Permanent link to this record
 

 
Author Daniel Ponsa; Antonio Lopez edit   pdf
openurl 
  Title Vehicle Trajectory Estimation based on Monocular Vision Type Conference Article
  Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 Abbreviated Journal  
  Volume Issue Pages 587-594  
  Keywords (up) vehicle detection  
  Abstract  
  Address Girona (Spain)  
  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 @ PoL2007a Serial 785  
Permanent link to this record
 

 
Author Daniel Ponsa; Antonio Lopez edit   pdf
openurl 
  Title Cascade of Classifiers for Vehicle Detection Type Conference Article
  Year 2007 Publication Advanced Concepts for Intelligent Vision Systems, LNCS 4678, volume 1, pp. 980–989 Abbreviated Journal  
  Volume Issue Pages  
  Keywords (up) vehicle detection  
  Abstract  
  Address Delft (Netherlands)  
  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 @ PoL2007c Serial 935  
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