toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
  Records Links
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  
  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 CORL  
  Notes ADAS Approved no  
  Call Number Admin @ si @ DRC2017 Serial 2988  
Permanent link to this record
 

 
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  
  Volume Issue Pages  
  Keywords  
  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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECMSM  
  Notes ADAS Approved no  
  Call Number Admin @ si @ VIS2017 Serial 2917  
Permanent link to this record
 

 
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  
  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 PAAMS  
  Notes ADAS Approved no  
  Call Number Admin @ si @ ASS2017 Serial 2918  
Permanent link to this record
 

 
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  
  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 Approved no  
  Call Number ADAS @ adas @ HEV2017a Serial 2879  
Permanent link to this record
 

 
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  
  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 WACV  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ HEV2017b Serial 2812  
Permanent link to this record
 

 
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 British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  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  
  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 BMVC  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ HSE2017a Serial 2945  
Permanent link to this record
 

 
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  
  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 ICLR  
  Notes ADAS; 600.085; 600.076; 601.281 Approved no  
  Call Number ADAS @ adas @ GKA2017 Serial 2815  
Permanent link to this record
 

 
Author Patricia Suarez; Angel Sappa; Boris Vintimilla edit   pdf
openurl 
  Title Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture Type Conference Article
  Year (down) 2017 Publication 19th international conference on image analysis and processing Abbreviated Journal  
  Volume Issue Pages  
  Keywords CNN in Multispectral Imaging; Image Colorization  
  Abstract This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results.  
  Address Catania; Italy; September 2017  
  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 ICIAP  
  Notes ADAS Approved no  
  Call Number Admin @ si @ SSV2017c Serial 3016  
Permanent link to this record
 

 
Author Patricia Suarez; Angel Sappa; Boris Vintimilla edit  doi
openurl 
  Title Cross-Spectral Image Patch Similarity using Convolutional Neural Network Type Conference Article
  Year (down) 2017 Publication IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach.
 
  Address San Sebastian; Spain; May 2017  
  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 ECMSM  
  Notes ADAS Approved no  
  Call Number Admin @ si @ SSV2017a Serial 2916  
Permanent link to this record
 

 
Author Patricia Suarez; Angel Sappa; Boris Vintimilla edit   pdf
openurl 
  Title Infrared Image Colorization based on a Triplet DCGAN Architecture Type Conference Article
  Year (down) 2017 Publication IEEE International Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time.
 
  Address Honolulu; Hawaii; USA; July 2017  
  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 CVPR  
  Notes ADAS Approved no  
  Call Number Admin @ si @ SSV2017b Serial 2920  
Permanent link to this record
Select All    Deselect All
 |   | 
Details

Save Citations:
Export Records: