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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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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. |
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Mountain View; CA; USA; November 2017 |
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CORL |
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ADAS; 600.085; 600.118 |
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Admin @ si @ DRC2017 |
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2988 |
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Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
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Title |
Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
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Conference Article |
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2016 |
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16th International Conference on Computational Science |
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80 |
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143-153 |
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Keywords |
Autonomous Driving; Stereo; CUDA; 3d reconstruction |
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Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
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San Diego; CA; USA; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ HCE2016a |
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2740 |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Evaluation of Similarity Functions in Multimodal Stereo |
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2012 |
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9th International Conference on Image Analysis and Recognition |
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7324 |
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I |
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320-329 |
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Aveiro, Portugal |
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This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-31294-6 |
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ICIAR |
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ADAS |
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BLS2012a |
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2014 |
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Author |
Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska |
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Title |
Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation |
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Conference Article |
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2018 |
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International MICCAI Brainlesion Workshop |
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11384 |
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393-405 |
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Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution |
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In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. |
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MICCAIW |
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ADAS; 600.118 |
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Admin @ si @ PSH2018 |
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3251 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Learning to Colorize Infrared Images |
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Conference Article |
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Year |
2017 |
Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent System |
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CNN in multispectral imaging; Image colorization |
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This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a 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. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach. |
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Porto; Portugal; June 2017 |
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ADAS; MSIAU; 600.086; 600.122; 600.118 |
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Admin @ si @ |
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2919 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture |
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Conference Article |
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2017 |
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19th international conference on image analysis and processing |
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CNN in Multispectral Imaging; Image Colorization |
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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. |
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Catania; Italy; September 2017 |
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ICIAP |
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ADAS; MSIAU; 600.086; 600.122; 600.118 |
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Admin @ si @ SSV2017c |
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3016 |
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Author |
Naveen Onkarappa; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa |
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Title |
Cross-spectral Stereo Correspondence using Dense Flow Fields |
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2014 |
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9th International Conference on Computer Vision Theory and Applications |
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3 |
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613-617 |
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Cross-spectral Stereo Correspondence; Dense Optical Flow; Infrared and Visible Spectrum |
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This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach. |
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Lisboa; Portugal; January 2014 |
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VISAPP |
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ADAS; 600.055; 600.076 |
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Admin @ si @ OAV2014 |
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2477 |
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Author |
Ariel Amato; Felipe Lumbreras; Angel Sappa |
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A General-purpose Crowdsourcing Platform for Mobile Devices |
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2014 |
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9th International Conference on Computer Vision Theory and Applications |
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3 |
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211-215 |
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Crowdsourcing Platform; Mobile Crowdsourcing |
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This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and tasksolver). Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications. Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform. |
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Lisboa; Portugal; January 2014 |
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ISE; ADAS; 600.054; 600.055; 600.076; 600.078 |
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Admin @ si @ ALS2014 |
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2478 |
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David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
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A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
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2017 |
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31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
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Deep Learning; Medical Imaging |
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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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CARS |
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ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
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ADAS @ adas @ VBS2017a |
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2880 |
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Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville |
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PixelVAE: A Latent Variable Model for Natural Images |
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2017 |
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5th International Conference on Learning Representations |
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Deep Learning; Unsupervised Learning |
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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. |
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Toulon; France; April 2017 |
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ADAS; 600.085; 600.076; 601.281; 600.118 |
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ADAS @ adas @ GKA2017 |
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2815 |
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