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Author Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy edit  openurl
  Title End-to-end Driving via Conditional Imitation Learning Type Conference Article
  Year (down) 2018 Publication International Conference on Robotics and Automation Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  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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  ISSN ISBN Medium  
  Area Expedition Conference ICRA  
  Notes ADAS; 600.116; 600.124 Approved no  
  Call Number Admin @ si @ CML2018 Serial 3108  
Permanent link to this record
 

 
Author Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun edit  openurl
  Title CARLA: An Open Urban Driving Simulator Type Conference Article
  Year (down) 2017 Publication 1st Annual Conference on Robot Learning Abbreviated Journal  
  Volume Issue Pages  
  Keywords Autonomous driving; sensorimotor control; simulation  
  Abstract We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research.
 
  Address Mountain View; CA; USA; November 2017  
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  ISSN ISBN Medium  
  Area Expedition Conference CORL  
  Notes ADAS; 600.085 Approved no  
  Call Number Admin @ si @ DRC2017 Serial 2988  
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Author Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa edit   pdf
openurl 
  Title A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers Type Conference Article
  Year (down) 2017 Publication IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics Abbreviated Journal  
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  Abstract In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point
that minimizes the vacuum force needed to guarantee a grasp.
Experimental results in real scenarios are presented to show the validity of the proposed approach.
 
  Address San Sebastian; Spain; May 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  ISSN ISBN Medium  
  Area Expedition Conference ECMSM  
  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ VIS2017 Serial 2917  
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Author Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo edit   pdf
openurl 
  Title RGBN Multispectral Images: a Novel Color Restoration Approach Type Conference Article
  Year (down) 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent System Abbreviated Journal  
  Volume Issue Pages  
  Keywords Multispectral Imaging; Free Sensor Model; Neural Network  
  Abstract This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided.
 
  Address Porto; Portugal; June 2017  
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  ISSN ISBN Medium  
  Area Expedition Conference PAAMS  
  Notes ADAS; no proj Approved no  
  Call Number Admin @ si @ ASS2017 Serial 2918  
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Author Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure edit   pdf
openurl 
  Title Embedded Real-time Stixel Computation Type Conference Article
  Year (down) 2017 Publication GPU Technology Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords GPU; CUDA; Stixels; Autonomous Driving  
  Abstract  
  Address Silicon Valley; USA; May 2017  
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  ISSN ISBN Medium  
  Area Expedition Conference GTC  
  Notes ADAS; no menciona Approved no  
  Call Number ADAS @ adas @ HEV2017a Serial 2879  
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Author Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure edit   pdf
openurl 
  Title GPU-accelerated real-time stixel computation Type Conference Article
  Year (down) 2017 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords Autonomous Driving; GPU; Stixel  
  Abstract The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multi-stixel estimation pipeline on an embedded, energyefficient, GPU-accelerated device. This work presents a full GPU-accelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024×440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card.  
  Address Santa Rosa; CA; USA; March 2017  
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  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes ADAS; no menciona Approved no  
  Call Number ADAS @ adas @ HEV2017b Serial 2812  
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Author Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure edit  openurl
  Title Slanted Stixels: Representing San Francisco's Steepest Streets} Type Conference Article
  Year (down) 2017 Publication 28th British Machine Vision Conference Abbreviated Journal  
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  Abstract In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.  
  Address London; uk; September 2017  
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  Area Expedition Conference BMVC  
  Notes ADAS; no menciona Approved no  
  Call Number ADAS @ adas @ HSE2017a Serial 2945  
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Author David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez-Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville edit   pdf
openurl 
  Title A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Type Conference Article
  Year (down) 2017 Publication 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal  
  Volume Issue Pages  
  Keywords Deep Learning; Medical Imaging  
  Abstract Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.  
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  Area Expedition Conference CARS  
  Notes ADAS; MV; 600.075; 600.085; 600.076; 601.281 Approved no  
  Call Number ADAS @ adas @ VBS2017a Serial 2880  
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Author Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville edit   pdf
url  openurl
  Title PixelVAE: A Latent Variable Model for Natural Images Type Conference Article
  Year (down) 2017 Publication 5th International Conference on Learning Representations Abbreviated Journal  
  Volume Issue Pages  
  Keywords Deep Learning; Unsupervised Learning  
  Abstract Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms.  
  Address Toulon; France; April 2017  
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  Area Expedition Conference ICLR  
  Notes ADAS; 600.085; 600.076; 601.281 Approved no  
  Call Number ADAS @ adas @ GKA2017 Serial 2815  
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Author Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis edit  openurl
  Title Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification Type Conference Article
  Year (down) 2017 Publication 3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition Abbreviated Journal  
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  Address Thessaloniki; Greece; October 2017  
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  ISSN ISBN Medium  
  Area Expedition Conference IMEKOFOODS  
  Notes ADAS; no menciona Approved no  
  Call Number Admin @ si @ GDM2017 Serial 3081  
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