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Author |
Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Metric Learning for Novelty and Anomaly Detection |
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Conference Article |
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Year |
2018 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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 |
German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
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Conference Article |
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Year |
2016 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
29th IEEE Conference on Computer Vision and Pattern Recognition |
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3234-3243 |
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Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
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Las Vegas; USA; June 2016 |
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CVPR |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ RSM2016 |
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2739 |
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Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo |
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Title |
Learning cross-spectral similarity measures with deep convolutional neural networks |
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Conference Article |
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2016 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
29th IEEE Conference on Computer Vision and Pattern Recognition Worshops |
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The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains. |
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Las vegas; USA; June 2016 |
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CVPRW |
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ADAS; 600.086; 600.076 |
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no |
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Admin @ si @AAS2016 |
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2809 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias |
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Title |
Scene Representations for Autonomous Driving: an approach based on polygonal primitives |
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Conference Article |
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2015 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2nd Iberian Robotics Conference ROBOT2015 |
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417 |
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503-515 |
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Scene reconstruction; Point cloud; Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Lisboa; Portugal; November 2015 |
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ROBOT |
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ADAS; 600.076; 600.086 |
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no |
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Admin @ si @ OSS2015a |
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2662 |
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Author |
J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Visible-Thermal Fusion based Monocular Visual Odometry |
Type |
Conference Article |
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Year |
2015 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2nd Iberian Robotics Conference ROBOT2015 |
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417 |
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517-528 |
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Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. |
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The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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Lisboa; Portugal; November 2015 |
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Springer International Publishing |
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2194-5357 |
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978-3-319-27145-3 |
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ROBOT |
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ADAS; 600.076; 600.086 |
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no |
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Admin @ si @ PAD2015 |
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2663 |
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Author |
Ariel Amato; Angel Sappa; Alicia Fornes; Felipe Lumbreras; Josep Llados |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform |
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Conference Article |
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Year |
2013 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2nd International ACM Workshop on Crowdsourcing for Multimedia |
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21-22 |
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In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized. |
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Barcelona; October 2013 |
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978-1-4503-2396-3 |
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CrowdMM |
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ADAS; ISE; DAG; 600.054; 600.055; 600.045; 600.061; 602.006 |
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Admin @ si @ SLA2013 |
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2335 |
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Author |
M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Cross-spectral image registration and fusion: an evaluation study |
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Conference Article |
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2015 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2nd International Conference on Machine Vision and Machine Learning |
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multispectral imaging; image registration; data fusion; infrared and visible spectra |
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This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Barcelona; July 2015 |
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MVML |
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ADAS; 600.076 |
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no |
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Admin @ si @ CAV2015 |
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2629 |
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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 |
Type |
Conference Article |
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2016 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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 |
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2906 |
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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 |
Type |
Conference Article |
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2017 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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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David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
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Conference Article |
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2017 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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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