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Author Antonio Lopez; Ernest Valveny; Juan J. Villanueva edit  url
openurl 
  Title Real-time quality control of surgical material packaging by artificial vision Type Journal
  Year 2005 Publication Assembly Automation 25(3): 223–229 (IF: 0.061) Abbreviated Journal  
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  Notes ADAS;DAG Approved no  
  Call Number ADAS @ adas @ LVV2005 Serial 552  
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Author Angel Sappa edit  url
openurl 
  Title Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation Type Journal
  Year 2006 Publication IEEE Transactions on Image Processing, 15(2):377–384 Abbreviated Journal  
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  Call Number ADAS @ adas @ Sap2006a Serial 637  
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Author A. Restrepo; Angel Sappa; M. Devy edit  url
openurl 
  Title Edge registration versus triangular mesh registration, a comparative study Type Journal
  Year 2005 Publication Signal Processing: Image Communication 20: 853–868 (IF: 1.264) Abbreviated Journal  
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  Call Number ADAS @ adas @ RSD2005 Serial 567  
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Author Jaume Amores; Petia Radeva edit  url
doi  openurl
  Title Registration and Retrieval of Highly Elastic Bodies using Contextual Information Type Journal Article
  Year 2005 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 26 Issue 11 Pages 1720–1731  
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  Abstract IF: 1.138  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ AmR2005b Serial 592  
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Author Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate edit  url
doi  openurl
  Title An overview of incremental feature extraction methods based on linear subspaces Type Journal Article
  Year 2018 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 145 Issue Pages 219-235  
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  Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.  
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  ISSN 0950-7051 ISBN Medium  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DFH2018 Serial 3090  
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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  
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  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 Admin @ si @ VWL2020 Serial 3405  
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Author Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure edit  url
openurl 
  Title Slanted Stixels: A way to represent steep streets Type Journal Article
  Year 2019 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 127 Issue Pages 1643–1658  
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  Abstract This work presents and evaluates 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 in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods 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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  Notes ADAS; 600.118; 600.124 Approved no  
  Call Number Admin @ si @ HSC2019 Serial 3304  
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Author Adrien Gaidon; Antonio Lopez; Florent Perronnin edit  url
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  Title The Reasonable Effectiveness of Synthetic Visual Data Type Journal Article
  Year 2018 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 126 Issue 9 Pages 899–901  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ GLP2018 Serial 3180  
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Author Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez edit  url
doi  openurl
  Title Semantic Monocular Depth Estimation Based on Artificial Intelligence Type Journal Article
  Year 2020 Publication IEEE Intelligent Transportation Systems Magazine Abbreviated Journal ITSM  
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  Abstract Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.  
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  Notes ADAS; 600.124 Approved no  
  Call Number Admin @ si @ GUH2019 Serial 3306  
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Author Zhijie Fang; Antonio Lopez edit  url
doi  openurl
  Title Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation Type Journal Article
  Year 2019 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
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  Abstract Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.  
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  Notes ADAS; no proj Approved no  
  Call Number Admin @ si @ FaL2019 Serial 3305  
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