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Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda |
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PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities |
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2015 |
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Journal of Technology and Science Education |
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JOTSE |
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5 |
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2 |
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87-96 |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ MRG2015 |
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2608 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Hierarchical Adaptive Structural SVM for Domain Adaptation |
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Journal Article |
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Year |
2016 |
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International Journal of Computer Vision |
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IJCV |
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119 |
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2 |
Pages |
159-178 |
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Domain Adaptation; Pedestrian Detection |
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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. |
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Springer US |
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0920-5691 |
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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XRV2016 |
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2669 |
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Author |
Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez |
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Title |
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models |
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2023 |
Publication |
Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” |
Abbreviated Journal |
SENS |
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23 |
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2 |
Pages |
621 |
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Keywords |
Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving |
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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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ADAS; no proj |
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Admin @ si @ GVL2023 |
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3705 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Title |
Detailed 3D face reconstruction from a single RGB image |
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2019 |
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Journal of WSCG |
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JWSCG |
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27 |
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2 |
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103-112 |
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3D Wrinkle Reconstruction; Face Analysis, Optimization. |
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This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles. |
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2019/11 |
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ADAS; 600.086; 600.130; 600.122 |
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Admin @ si @ |
Serial |
3708 |
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Author |
Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
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Title |
Monitoring war destruction from space using machine learning |
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Journal Article |
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Year |
2021 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
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PNAS |
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Volume |
118 |
Issue |
23 |
Pages |
e2025400118 |
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Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available. |
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ADAS; 600.118 |
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Admin @ si @ MGH2021 |
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3584 |
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Author |
J. Pladellorens; M.J. Yzuel; J. Castell; Joan Serrat |
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Title |
Calculo automatico del volumen del ventriculo izquierdo. Comparacion con expertos. |
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1993 |
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Optica Pura y Aplicada. |
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26 |
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3 |
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685–691 |
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ADAS |
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ADAS @ adas @ PYC1993 |
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149 |
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Author |
Antonio Lopez; Ernest Valveny; Juan J. Villanueva |
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Real-time quality control of surgical material packaging by artificial vision |
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2005 |
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Assembly Automation |
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25 |
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3 |
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Abstract |
IF: 0.061) |
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ADAS;DAG |
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ADAS @ adas @ LVV2005 |
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552 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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3 |
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201–209 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2006 |
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643 |
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Author |
Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez |
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An Efficient Approach to Onboard Stereo Vision System Pose Estimation |
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2008 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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9 |
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3 |
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476–490 |
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Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system |
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This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results. |
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IEEE |
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ADAS |
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ADAS @ adas @ SDP2008 |
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1000 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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An iterative multiresolution scheme for SFM with missing data |
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2009 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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34 |
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3 |
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240–258 |
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Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach. |
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ADAS |
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ADAS @ adas @ JSL2009a |
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1163 |
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