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Author Jiaolong Xu; Liang Xiao; Antonio Lopez edit  doi
openurl 
  Title Self-supervised Domain Adaptation for Computer Vision Tasks Type Journal Article
  Year 2019 Publication IEEE ACCESS Abbreviated Journal ACCESS  
  Volume 7 Issue Pages 156694 - 156706  
  Keywords  
  Abstract Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da.  
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  Notes ADAS; no proj Approved no  
  Call Number (down) Admin @ si @ XXL2019 Serial 3302  
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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Hierarchical Adaptive Structural SVM for Domain Adaptation Type Journal Article
  Year 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 119 Issue 2 Pages 159-178  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition.
 
  Address  
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  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number (down) Admin @ si @ XRV2016 Serial 2669  
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Author Gabriel Villalonga; Joost van de Weijer; Antonio Lopez edit  url
doi  openurl
  Title Recognizing new classes with synthetic data in the loop: application to traffic sign recognition Type Journal Article
  Year 2020 Publication Sensors Abbreviated Journal SENS  
  Volume 20 Issue 3 Pages 583  
  Keywords  
  Abstract On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive.  
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  Notes LAMP; ADAS; no proj Approved no  
  Call Number (down) Admin @ si @ VWL2020 Serial 3405  
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Author Victor Santos; Angel Sappa; Miguel Oliveira edit  openurl
  Title Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
  Year 2017 Publication Robotics and Autonomous Systems Abbreviated Journal RAS  
  Volume 91 Issue Pages 208-209  
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  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number (down) Admin @ si @ SSO2017 Serial 2915  
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Author Xavier Soria; Angel Sappa; Riad I. Hammoud edit  url
doi  openurl
  Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images Type Journal Article
  Year 2018 Publication Sensors Abbreviated Journal SENS  
  Volume 18 Issue 7 Pages 2059  
  Keywords RGB-NIR sensor; multispectral imaging; deep learning; CNNs  
  Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
 
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  Notes ADAS: MSIAU; 600.086; 600.130; 600.122; 600.118 Approved no  
  Call Number (down) Admin @ si @ SSH2018 Serial 3145  
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Author Joan Serrat; Felipe Lumbreras; Idoia Ruiz edit  url
openurl 
  Title Learning to measure for preshipment garment sizing Type Journal Article
  Year 2018 Publication Measurement Abbreviated Journal MEASURE  
  Volume 130 Issue Pages 327-339  
  Keywords Apparel; Computer vision; Structured prediction; Regression  
  Abstract Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.  
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  Notes ADAS: MSIAU; 600.122; 600.118 Approved no  
  Call Number (down) Admin @ si @ SLR2018 Serial 3128  
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Author Joan Serrat; Felipe Lumbreras; Antonio Lopez edit   pdf
doi  openurl
  Title Cost estimation of custom hoses from STL files and CAD drawings Type Journal Article
  Year 2013 Publication Computers in Industry Abbreviated Journal COMPUTIND  
  Volume 64 Issue 3 Pages 299-309  
  Keywords On-line quotation; STL format; Regression; Gaussian process  
  Abstract We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%.  
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  Publisher Elsevier Place of Publication Editor  
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  Notes ADAS; 600.057; 600.054; 605.203 Approved no  
  Call Number (down) Admin @ si @ SLL2013; ADAS @ adas @ Serial 2161  
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Author Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas edit  url
openurl 
  Title myStone: A system for automatic kidney stone classification Type Journal Article
  Year 2017 Publication Expert Systems with Applications Abbreviated Journal ESA  
  Volume 89 Issue Pages 41-51  
  Keywords Kidney stone; Optical device; Computer vision; Image classification  
  Abstract Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size.  
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  Notes ADAS: MSIAU; 603.046; 600.122; 600.118 Approved no  
  Call Number (down) Admin @ si @ SLB2017 Serial 3026  
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Author Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez edit  doi
openurl 
  Title Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models Type Journal Article
  Year 2019 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume Issue Pages  
  Keywords Procedural generation; Human action recognition; Synthetic data; Physics  
  Abstract Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos.  
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  Notes ADAS; 600.124 Approved no  
  Call Number (down) Admin @ si @ SGC2019 Serial 3303  
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Author Arnau Ramisa; Adriana Tapus; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors Type Journal Article
  Year 2009 Publication Autonomous Robots Abbreviated Journal AR  
  Volume 27 Issue 4 Pages 373-385  
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  Abstract This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.
In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.
 
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  Series Volume Series Issue Edition  
  ISSN 0929-5593 ISBN Medium  
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  Notes ADAS Approved no  
  Call Number (down) Admin @ si @ RTA2009 Serial 1245  
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