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Author Carme Julia; Felipe Lumbreras; Angel Sappa edit  doi
openurl 
  Title A Factorization-based Approach to Photometric Stereo Type Journal Article
  Year 2011 Publication International Journal of Imaging Systems and Technology Abbreviated Journal IJIST  
  Volume 21 Issue 1 Pages 115-119  
  Keywords  
  Abstract This article presents an adaptation of a factorization technique to tackle the photometric stereo problem. That is to recover the surface normals and reflectance of an object from a set of images obtained under different lighting conditions. The main contribution of the proposed approach is to consider pixels in shadow and saturated regions as missing data, in order to reduce their influence to the result. Concretely, an adapted Alternation technique is used to deal with missing data. Experimental results considering both synthetic and real images show the viability of the proposed factorization-based strategy. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 115–119, 2011.  
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  Notes ADAS Approved no  
  Call Number (down) Admin @ si @ JLS2011; ADAS @ adas @ Serial 1711  
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Author Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil edit  doi
openurl 
  Title Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals Type Journal Article
  Year 2022 Publication Applied Sciences Abbreviated Journal APPLSCI  
  Volume 12 Issue 5 Pages 2298  
  Keywords Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion  
  Abstract The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.  
  Address February 2022  
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  Notes IAM; ADAS; 600.139; 600.145; 600.118 Approved no  
  Call Number (down) Admin @ si @ HYF2022 Serial 3720  
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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   pdf
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  
  Keywords  
  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 (down) Admin @ si @ HSC2019 Serial 3304  
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez edit  doi
openurl 
  Title CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool Type Journal Article
  Year 2015 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 18 Issue 1 Pages 15-30  
  Keywords  
  Abstract Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research.  
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  Publisher Springer Berlin Heidelberg Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium  
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  Notes DAG; ADAS; 600.061; 600.076; 600.077 Approved no  
  Call Number (down) Admin @ si @ HRR2015 Serial 2567  
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Author Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin edit   pdf
url  openurl
  Title Mathematics learning opportunities when playing a Tower Defense Game Type Journal
  Year 2015 Publication International Journal of Serious Games Abbreviated Journal IJSG  
  Volume 2 Issue 4 Pages 57-71  
  Keywords Tower Defense game; learning opportunities; mathematics; problem solving; game design  
  Abstract A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.  
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  Notes ADAS; 600.076 Approved no  
  Call Number (down) Admin @ si @ HJG2015 Serial 2730  
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Author Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure edit   pdf
url  doi
openurl 
  Title 3D Perception With Slanted Stixels on GPU Type Journal Article
  Year 2021 Publication IEEE Transactions on Parallel and Distributed Systems Abbreviated Journal TPDS  
  Volume 32 Issue 10 Pages 2434-2447  
  Keywords Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure  
  Abstract This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier.  
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  Notes ADAS; 600.124; 600.118 Approved no  
  Call Number (down) Admin @ si @ HEV2021 Serial 3561  
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Author Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez edit  url
openurl 
  Title Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models Type Journal Article
  Year 2023 Publication Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” Abbreviated Journal SENS  
  Volume 23 Issue 2 Pages 621  
  Keywords Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving  
  Abstract Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic
segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall
procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our
procedure shows improvements ranging from ∼13 to ∼26 mIoU points over baselines, so establishing new state-of-the-art results.
 
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  Notes ADAS; no proj Approved no  
  Call Number (down) Admin @ si @ GVL2023 Serial 3705  
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Author Jose L. Gomez; Gabriel Villalonga; Antonio Lopez edit   pdf
url  openurl
  Title Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches Type Journal Article
  Year 2021 Publication Sensors Abbreviated Journal SENS  
  Volume 21 Issue 9 Pages 3185  
  Keywords co-training; multi-modality; vision-based object detection; ADAS; self-driving  
  Abstract Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.  
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  Notes ADAS; 600.118 Approved no  
  Call Number (down) Admin @ si @ GVL2021 Serial 3562  
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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  
  Volume 13 Issue 4 Pages 99-103  
  Keywords  
  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; 600.118 Approved no  
  Call Number (down) Admin @ si @ GUH2019 Serial 3306  
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Author Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez edit   pdf
doi  openurl
  Title Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision Type Journal Article
  Year 2021 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 23 Issue 8 Pages 12738-12751  
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  Abstract Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.  
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  Notes ADAS; 600.118 Approved no  
  Call Number (down) Admin @ si @ GTS2021 Serial 3598  
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