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Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo |
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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Conference Article |
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2017 |
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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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Admin @ si @ ASS2017 |
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2918 |
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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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2017 |
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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 @ |
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2919 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture |
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Conference Article |
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2017 |
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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 |
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2920 |
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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 |
Publication |
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 |
Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez |
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Title |
Procedural Generation of Videos to Train Deep Action Recognition Networks |
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Conference Article |
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2017 |
Publication |
30th IEEE Conference on Computer Vision and Pattern Recognition |
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2594-2604 |
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Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
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Honolulu; Hawaii; July 2017 |
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CVPR |
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ADAS; 600.076; 600.085; 600.118 |
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Admin @ si @ SGC2017 |
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3051 |
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Author |
Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis |
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Title |
Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification |
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Conference Article |
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2017 |
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3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition |
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Thessaloniki; Greece; October 2017 |
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IMEKOFOODS |
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ADAS; 600.118 |
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Admin @ si @ GDM2017 |
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3081 |
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Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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Title |
End-to-end Driving via Conditional Imitation Learning |
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Conference Article |
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2018 |
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IEEE International Conference on Robotics and Automation |
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4693 - 4700 |
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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 |
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Brisbane; Australia; May 2018 |
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ICRA |
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ADAS; 600.116; 600.124; 600.118 |
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Admin @ si @ CML2018 |
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3108 |
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Author |
Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez |
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Title |
Metric Learning for Novelty and Anomaly Detection |
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Conference Article |
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2018 |
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29th British Machine Vision Conference |
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When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works. |
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Newcastle; uk; September 2018 |
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BMVC |
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LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 |
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Admin @ si @ MRS2018 |
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3156 |
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Author |
Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov |
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Title |
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting |
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Conference Article |
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2018 |
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24th International Conference on Pattern Recognition |
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2262-2268 |
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In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and
Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting. |
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LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 |
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Admin @ si @ LMH2018 |
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3160 |
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Author |
Zhijie Fang; Antonio Lopez |
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Title |
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation |
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Conference Article |
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2018 |
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IEEE Intelligent Vehicles Symposium |
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1271 - 1276 |
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Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ FaL2018 |
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3181 |
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