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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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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 |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
3016 |
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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
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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 |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2988 |
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Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Slanted Stixels: Representing San Francisco's Steepest Streets} |
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Conference Article |
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2017 |
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28th British Machine Vision Conference |
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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. |
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London; uk; September 2017 |
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BMVC |
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ADAS; 600.118 |
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ADAS @ adas @ HSE2017a |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2945 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture |
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Conference Article |
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Year |
2017 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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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. |
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Honolulu; Hawaii; USA; July 2017 |
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CVPRW |
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ADAS; 600.086; 600.118 |
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Admin @ si @ SSV2017b |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2920 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Learning to Colorize Infrared Images |
Type |
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 dierent 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 @ |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2919 |
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Permanent link to this record |
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Author |
Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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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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Multispectral Imaging; Free Sensor Model; Neural Network |
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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. |
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Porto; Portugal; June 2017 |
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ADAS; MSIAU; 600.118; 600.122 |
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no |
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Admin @ si @ ASS2017 |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2918 |
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Permanent link to this record |
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Author |
Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
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Conference Article |
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2017 |
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IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
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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. |
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San Sebastian; Spain; May 2017 |
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ECMSM |
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ADAS; 600.086; 600.118 |
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no |
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Admin @ si @ VIS2017 |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2917 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Cross-Spectral Image Patch Similarity using Convolutional Neural Network |
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Conference Article |
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2017 |
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IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
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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. |
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San Sebastian; Spain; May 2017 |
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ADAS; 600.086; 600.118 |
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Admin @ si @ SSV2017a |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2916 |
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Author |
Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa |
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Title |
Fine-tuning based deep convolutional networks for lepidopterous genus recognition |
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Conference Article |
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2016 |
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21st Ibero American Congress on Pattern Recognition |
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467-475 |
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This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. |
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Lima; Perú; November 2016 |
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CIARP |
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ADAS; 600.086 |
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Admin @ si @ CRS2016 |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2913 |
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Author |
Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Invertible conditional gans for image editing |
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Conference Article |
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2016 |
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30th Annual Conference on Neural Information Processing Systems Worshops |
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Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications. |
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Barcelona; Spain; December 2016 |
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NIPSW |
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LAMP; ADAS; 600.068 |
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Admin @ si @ PWR2016 |
Serial ![sorted by Serial field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
2906 |
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