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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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2021 |
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Proceedings of the National Academy of Sciences of the United States of America |
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PNAS |
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118 |
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23 |
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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 |
Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
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Title |
Monitoring War Destruction from Space: A Machine Learning Approach |
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2020 |
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Arxiv |
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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 data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. |
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ADAS; 600.118 |
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Admin @ si @ MGH2020 |
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3489 |
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Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Monocular 3D Human Body Reconstruction Towards Depth Augmentation of Television Sequences |
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2003 |
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IEEE International Conference on Image Processing, Barcelona, Spain, September 2003 |
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325-328 |
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Barcelona |
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ADAS @ adas @ SAM2003 |
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418 |
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Author |
Diego Alejandro Cheda |
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Title |
Monocular Depth Cues in Computer Vision Applications |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Depth perception is a key aspect of human vision. It is a routine and essential visual task that the human do effortlessly in many daily activities. This has often been associated with stereo vision, but humans have an amazing ability to perceive depth relations even from a single image by using several monocular cues.
In the computer vision field, if image depth information were available, many tasks could be posed from a different perspective for the sake of higher performance and robustness. Nevertheless, given a single image, this possibility is usually discarded, since obtaining depth information has frequently been performed by three-dimensional reconstruction techniques, requiring two or more images of the same scene taken from different viewpoints. Recently, some proposals have shown the feasibility of computing depth information from single images. In essence, the idea is to take advantage of a priori knowledge of the acquisition conditions and the observed scene to estimate depth from monocular pictorial cues. These approaches try to precisely estimate the scene depth maps by employing computationally demanding techniques. However, to assist many computer vision algorithms, it is not really necessary computing a costly and detailed depth map of the image. Indeed, just a rough depth description can be very valuable in many problems.
In this thesis, we have demonstrated how coarse depth information can be integrated in different tasks following alternative strategies to obtain more precise and robust results. In that sense, we have proposed a simple, but reliable enough technique, whereby image scene regions are categorized into discrete depth ranges to build a coarse depth map. Based on this representation, we have explored the potential usefulness of our method in three application domains from novel viewpoints: camera rotation parameters estimation, background estimation and pedestrian candidate generation. In the first case, we have computed camera rotation mounted in a moving vehicle applying two novels methods based on distant elements in the image, where the translation component of the image flow vectors is negligible. In background estimation, we have proposed a novel method to reconstruct the background by penalizing close regions in a cost function, which integrates color, motion, and depth terms. Finally, we have benefited of geometric and depth information available on single images for pedestrian candidate generation to significantly reduce the number of generated windows to be further processed by a pedestrian classifier. In all cases, results have shown that our approaches contribute to better performances. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Daniel Ponsa;Antonio Lopez |
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ADAS |
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Admin @ si @ Che2012 |
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2210 |
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Author |
Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
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Title |
Monocular Depth Estimation by Learning from Heterogeneous Datasets |
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Conference Article |
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2018 |
Publication |
IEEE Intelligent Vehicles Symposium |
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2176 - 2181 |
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Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). 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, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, 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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IV |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ GUH2018 |
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3183 |
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Author |
Akhil Gurram |
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Title |
Monocular Depth Estimation for Autonomous Driving |
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2022 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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3D geometric information is essential for on-board perception in autonomous driving and driver assistance. Autonomous vehicles (AVs) are equipped with calibrated sensor suites. As part of these suites, we can find LiDARs, which are expensive active sensors in charge of providing the 3D geometric information. Depending on the operational conditions for the AV, calibrated stereo rigs may be also sufficient for obtaining 3D geometric information, being these rigs less expensive and easier to install than LiDARs. However, ensuring a proper maintenance and calibration of these types of sensors is not trivial. Accordingly, there is an increasing interest on performing monocular depth estimation (MDE) to obtain 3D geometric information on-board. MDE is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Moreover, a set of single cameras with MDE capabilities would still be a cheap solution for on-board perception, relatively easy to integrate and maintain in an AV.
Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Accordingly, the overall goal of this PhD is to study methods for improving CNN-based MDE accuracy under different training settings. More specifically, this PhD addresses different research questions that are described below. When we started to work in this PhD, state-of-theart methods for MDE were already based on CNNs. In fact, a promising line of work consisted in using image-based semantic supervision (i.e., pixel-level class labels) while training CNNs for MDE using LiDAR-based supervision (i.e., depth). It was common practice to assume that the same raw training data are complemented by both types of supervision, i.e., with depth and semantic labels. However, in practice, it was more common to find heterogeneous datasets with either only depth supervision or only semantic supervision. Therefore, our first work was to research if we could train CNNs for MDE by leveraging depth and semantic information from heterogeneous datasets. We show that this is indeed possible, and we surpassed the state-of-the-art results on MDE at the time we did this research. To achieve our results, we proposed a particular CNN architecture and a new training protocol.
After this research, it was clear that the upper-bound setting to train CNN-based MDE models consists in using LiDAR data as supervision. However, it would be cheaper and more scalable if we would be able to train such models from monocular sequences. Obviously, this is far more challenging, but worth to research. Training MDE models using monocular sequences 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. To alleviate these problems, we perform MDE by virtual-world supervision and real-world SfM self-supervision. We call our proposalMonoDEVSNet. We compensate the SfM self-supervision limitations by leveraging
virtual-world images with accurate semantic and depth supervision, as well as addressing the virtual-to-real domain gap. MonoDEVSNet outperformed previous MDE CNNs trained on monocular and even stereo sequences. We have publicly released MonoDEVSNet at <https://github.com/HMRC-AEL/MonoDEVSNet>.
Finally, since MDE is performed to produce 3D information for being used in downstream tasks related to on-board perception. We also address the question of whether the standard metrics for MDE assessment are a good indicator for future MDE-based driving-related perception tasks. By using 3D object detection on point clouds as proxy of on-board perception, we conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods which reflects relatively well the 3D object detection results we may expect. |
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March, 2022 |
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Ph.D. thesis |
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IMPRIMA |
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Antonio Lopez;Onay Urfalioglu |
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978-84-124793-0-0 |
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ADAS |
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Admin @ si @ Gur2022 |
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3712 |
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Author |
Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez |
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Title |
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision |
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Journal Article |
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2021 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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23 |
Issue |
8 |
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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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ADAS; 600.118 |
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Admin @ si @ GTS2021 |
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3598 |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Monocular Depth-based Background Estimation |
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Conference Article |
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2012 |
Publication |
7th International Conference on Computer Vision Theory and Applications |
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323-328 |
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In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences. |
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Roma |
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VISAPP |
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ADAS |
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Admin @ si @ CPL2012b; ADAS @ adas @ cpl2012e |
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2012 |
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Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Monocular Egomotion Estimation based on Image Matching |
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2012 |
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1st International Conference on Pattern Recognition Applications and Methods |
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425-430 |
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Portugal |
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ADAS |
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Admin @ si @ CPL2012a;; ADAS @ adas @ |
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2011 |
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Diego Alejandro Cheda |
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Monocular egomotion estimation for ADAS application |
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2009 |
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CVC Technical Report |
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148 |
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Computer Vision Center |
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Ph.D. thesis |
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Bellaterra, Barcelona |
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ADAS |
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Admin @ si @ Che2009 |
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2402 |
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Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
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Monocular visual odometry: A cross-spectral image fusion based approach |
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2016 |
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Robotics and Autonomous Systems |
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RAS |
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85 |
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26-36 |
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Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion |
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This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
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Elsevier B.V. |
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ADAS;600.086; 600.076 |
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Admin @ si @SAC2016 |
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2811 |
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Jordi Vitria; X. Binefa; Juan J. Villanueva |
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Morphological Algorithms for Visual Analysis of Integrated Circuits. |
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Miscellaneous |
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1992 |
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Journal of Visual Communications and image Representation, Vol.3, No.2, pp.194–202. |
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Notes |
OR;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ VBV1992 |
Serial |
248 |
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| Permanent link to this record |
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Author |
D. Seron; F. Moreso; C. Gratin; Jordi Vitria |
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| |
Title |
Morphological Granulometries and Quantification of Interstitial Chronic Renal Damage |
Type |
Miscellaneous |
| |
Year |
1995 |
Publication |
VI National Simposium on Pattern Recognition and image Analysis. |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Keywords |
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Abstract |
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Address |
Cordoba |
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Corporate Author |
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Notes |
OR;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ SMG1995 |
Serial |
137 |
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| Permanent link to this record |
| |
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| |
|
Author |
Jordi Vitria; C. Gratin; D. Seron; F. Moreso |
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| |
Title |
Morphological image analysis for quantification of renal damage |
Type |
Report |
| |
Year |
1995 |
Publication |
CVC Technical Report #02 |
Abbreviated Journal |
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| |
Volume |
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Issue |
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Pages |
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Keywords |
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Abstract |
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Address |
CVC (UAB) |
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Conference |
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| |
Notes |
OR;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ VGS1995 |
Serial |
117 |
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| Permanent link to this record |
| |
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| |
|
Author |
Joan Serrat; Jordi Vitria; J. Pladellorens |
|
| |
Title |
Morphological Segmentation of Heart Scintigraphic image Sequences. |
Type |
Conference Article |
| |
Year |
1991 |
Publication |
Computer Assisted Radiology. |
Abbreviated Journal |
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| |
Volume |
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Issue |
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Pages |
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Keywords |
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Abstract |
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Address |
Berlin |
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Corporate Author |
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Thesis |
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Conference |
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| |
Notes |
ADAS;OR;MV |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ SVP1991 |
Serial |
263 |
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| Permanent link to this record |
