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| Author | Aitor Alvarez-Gila | ||||
| Title | Self-supervised learning for image-to-image translation in the small data regime | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Computer vision; Neural networks; Self-supervised learning; Image-to-image mapping; Probabilistic programming | ||||
| Abstract | The mass irruption of Deep Convolutional Neural Networks (CNNs) in computer vision since 2012 led to a dominance of the image understanding paradigm consisting in an end-to-end fully supervised learning workflow over large-scale annotated datasets. This approach proved to be extremely useful at solving a myriad of classic and new computer vision tasks with unprecedented performance —often, surpassing that of humans—, at the expense of vast amounts of human-labeled data, extensive computational resources and the disposal of all of our prior knowledge on the task at hand. Even though simple transfer learning methods, such as fine-tuning, have achieved remarkable impact, their success when the amount of labeled data in the target domain is small is limited. Furthermore, the non-static nature of data generation sources will often derive in data distribution shifts that degrade the performance of deployed models. As a consequence, there is a growing demand for methods that can exploit elements of prior knowledge and sources of information other than the manually generated ground truth annotations of the images during the network training process, so that they can adapt to new domains that constitute, if not a small data regime, at least a small labeled data regime. This thesis targets such few or no labeled data scenario in three distinct image-to-image mapping learning problems. It contributes with various approaches that leverage our previous knowledge of different elements of the image formation process: We first present a data-efficient framework for both defocus and motion blur detection, based on a model able to produce realistic synthetic local degradations. The framework comprises a self-supervised, a weakly-supervised and a semi-supervised instantiation, depending on the absence or availability and the nature of human annotations, and outperforms fully-supervised counterparts in a variety of settings. Our knowledge on color image formation is then used to gather input and target ground truth image pairs for the RGB to hyperspectral image reconstruction task. We make use of a CNN to tackle this problem, which, for the first time, allows us to exploit spatial context and achieve state-of-the-art results given a limited hyperspectral image set. In our last contribution to the subfield of data-efficient image-to-image transformation problems, we present the novel semi-supervised task of zero-pair cross-view semantic segmentation: we consider the case of relocation of the camera in an end-to-end trained and deployed monocular, fixed-view semantic segmentation system often found in industry. Under the assumption that we are allowed to obtain an additional set of synchronized but unlabeled image pairs of new scenes from both original and new camera poses, we present ZPCVNet, a model and training procedure that enables the production of dense semantic predictions in either source or target views at inference time. The lack of existing suitable public datasets to develop this approach led us to the creation of MVMO, a large-scale Multi-View, Multi-Object path-traced dataset with per-view semantic segmentation annotations. We expect MVMO to propel future research in the exciting under-developed fields of cross-view and multi-view semantic segmentation. Last, in a piece of applied research of direct application in the context of process monitoring of an Electric Arc Furnace (EAF) in a steelmaking plant, we also consider the problem of simultaneously estimating the temperature and spectral emissivity of distant hot emissive samples. To that end, we design our own capturing device, which integrates three point spectrometers covering a wide range of the Ultra-Violet, visible, and Infra-Red spectra and is capable of registering the radiance signal incoming from an 8cm diameter spot located up to 20m away. We then define a physically accurate radiative transfer model that comprises the effects of atmospheric absorbance, of the optical system transfer function, and of the sample temperature and spectral emissivity themselves. We solve this inverse problem without the need for annotated data using a probabilistic programming-based Bayesian approach, which yields full posterior distribution estimates of the involved variables that are consistent with laboratory-grade measurements. | ||||
| Address | Julu, 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Joost Van de Weijer; Estibaliz Garrote | ||
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| Notes | LAMP | Approved | no | ||
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Admin @ si @ Alv2022 | Serial | 3716 | ||
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| Author | Ariel Amato | ||||
| Title | Multiple Camera Calibration for Trajectories Tracking | Type | Report | ||
| Year | 2007 | Publication | CVC Technical Report #112 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ Ama2007a | Serial | 824 | ||
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| Author | Ariel Amato | ||||
| Title | Environment-Independent Moving Cast Shadow Suppression in Video Surveillance | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | This thesis is devoted to moving shadows detection and suppression. Shadows could be defined as the parts of the scene that are not directly illuminated by a light source due to obstructing object or objects. Often, moving shadows in images sequences are undesirable since they could cause degradation of the expected results during processing of images for object detection, segmentation, scene surveillance or similar purposes. In this thesis first moving shadow detection methods are exhaustively overviewed. Beside the mentioned methods from literature and to compensate their limitations a new moving shadow detection method is proposed. It requires no prior knowledge about the scene, nor is it restricted to assumptions about specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene the values of the background image are divided by values of the current frame in the RGB color space. In the thesis how this luminance ratio can be used to identify segments with low gradient constancy is shown, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of the proposed method compared with the most sophisticated state-of-the-art shadow detection algorithms. These results show that the proposed approach is robust and accurate over a broad range of shadow types and challenging video conditions. | ||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Mikhail Mozerov;Jordi Gonzalez | |
| Language | Summary Language | Original Title | |||
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ Ama2012 | Serial | 2201 | ||
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| Author | Ariel Amato | ||||
| Title | Moving cast shadow detection | Type | Journal Article | ||
| Year | 2014 | Publication | Electronic letters on computer vision and image analysis | Abbreviated Journal | ELCVIA |
| Volume | 13 | Issue | 2 | Pages | 70-71 |
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| Abstract | Motion perception is an amazing innate ability of the creatures on the planet. This adroitness entails a functional advantage that enables species to compete better in the wild. The motion perception ability is usually employed at different levels, allowing from the simplest interaction with the ’physis’ up to the most transcendental survival tasks. Among the five classical perception system , vision is the most widely used in the motion perception field. Millions years of evolution have led to a highly specialized visual system in humans, which is characterized by a tremendous accuracy as well as an extraordinary robustness. Although humans and an immense diversity of species can distinguish moving object with a seeming simplicity, it has proven to be a difficult and non trivial problem from a computational perspective. In the field of Computer Vision, the detection of moving objects is a challenging and fundamental research area. This can be referred to as the ’origin’ of vast and numerous vision-based research sub-areas. Nevertheless, from the bottom to the top of this hierarchical analysis, the foundations still relies on when and where motion has occurred in an image. Pixels corresponding to moving objects in image sequences can be identified by measuring changes in their values. However, a pixel’s value (representing a combination of color and brightness) could also vary due to other factors such as: variation in scene illumination, camera noise and nonlinear sensor responses among others. The challenge lies in detecting if the changes in pixels’ value are caused by a genuine object movement or not. An additional challenging aspect in motion detection is represented by moving cast shadows. The paradox arises because a moving object and its cast shadow share similar motion patterns. However, a moving cast shadow is not a moving object. In fact, a shadow represents a photometric illumination effect caused by the relative position of the object with respect to the light sources. Shadow detection methods are mainly divided in two domains depending on the application field. One normally consists of static images where shadows are casted by static objects, whereas the second one is referred to image sequences where shadows are casted by moving objects. For the first case, shadows can provide additional geometric and semantic cues about shape and position of its casting object as well as the localization of the light source. Although the previous information can be extracted from static images as well as video sequences, the main focus in the second area is usually change detection, scene matching or surveillance. In this context, a shadow can severely affect with the analysis and interpretation of the scene. The work done in the thesis is focused on the second case, thus it addresses the problem of detection and removal of moving cast shadows in video sequences in order to enhance the detection of moving object. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ Ama2014 | Serial | 2870 | ||
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| Author | Ariel Amato; Mikhail Mozerov; Andrew Bagdanov; Jordi Gonzalez | ||||
| Title | Accurate Moving Cast Shadow Suppression Based on Local Color Constancy detection | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 20 | Issue | 10 | Pages | 2954 - 2966 |
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| Abstract | This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions. | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ AMB2011 | Serial | 1716 | ||
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| Author | Egils Avots; Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Baro; Paul Pallin; Gholamreza Anbarjafari | ||||
| Title | From 2D to 3D geodesic-based garment matching | Type | Journal Article | ||
| Year | 2019 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 78 | Issue | 18 | Pages | 25829–25853 |
| Keywords | Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model | ||||
| Abstract | A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset. | ||||
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| Notes | HuPBA; ISE; 600.098; 600.119; 602.133 | Approved | no | ||
| Call Number |
Admin @ si @ AME2019 | Serial | 3317 | ||
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| Author | O.F.Ahmad; Y.Mori; M.Misawa; S.Kudo; J.T.Anderson; Jorge Bernal | ||||
| Title | Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method | Type | Journal Article | ||
| Year | 2021 | Publication | Endoscopy | Abbreviated Journal | END |
| Volume | 53 | Issue | 9 | Pages | 893-901 |
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| Abstract | BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ AMM2021 | Serial | 3670 | ||
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| Author | Jaume Amores | ||||
| Title | Multiple Instance Classification: review, taxonomy and comparative study | Type | Journal Article | ||
| Year | 2013 | Publication | Artificial Intelligence | Abbreviated Journal | AI |
| Volume | 201 | Issue | Pages | 81-105 | |
| Keywords | Multi-instance learning; Codebook; Bag-of-Words | ||||
| Abstract | Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods. |
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| Publisher | Elsevier Science Publishers Ltd. Essex, UK | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0004-3702 | ISBN | Medium | ||
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| Notes | ADAS; 601.042; 600.057 | Approved | no | ||
| Call Number |
Admin @ si @ Amo2013 | Serial | 2273 | ||
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| Author | Jaume Amores | ||||
| Title | MILDE: multiple instance learning by discriminative embedding | Type | Journal Article | ||
| Year | 2015 | Publication | Knowledge and Information Systems | Abbreviated Journal | KAIS |
| Volume | 42 | Issue | 2 | Pages | 381-407 |
| Keywords | Multi-instance learning; Codebook; Bag of words | ||||
| Abstract | While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. | ||||
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| Publisher | Springer London | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0219-1377 | ISBN | Medium | ||
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| Notes | ADAS; 601.042; 600.057; 600.076 | Approved | no | ||
| Call Number |
Admin @ si @ Amo2015 | Serial | 2383 | ||
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| Author | Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | Human-Robot Interaction: Theory and Application | Type | Book Whole | ||
| Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
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| ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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| Notes | HUPBA | Approved | no | ||
| Call Number |
Admin @ si @ AnE2018 | Serial | 3216 | ||
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| Author | Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva | ||||
| Title | Uncertainty Modeling and Deep Learning Applied to Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 13th International Joint Conference on Biomedical Engineering Systems and Technologies | Abbreviated Journal | |
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| Abstract | Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. | ||||
| Address | Villetta; Malta; February 2020 | ||||
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| Area | Expedition | Conference | BIODEVICES | ||
| Notes | MILAB | Approved | no | ||
| Call Number |
Admin @ si @ ANK2020 | Serial | 3526 | ||
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| Author | Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva | ||||
| Title | Bayesian deep learning for semantic segmentation of food images | Type | Journal Article | ||
| Year | 2022 | Publication | Computers and Electrical Engineering | Abbreviated Journal | CEE |
| Volume | 103 | Issue | Pages | 108380 | |
| Keywords | Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis | ||||
| Abstract | Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. | ||||
| Address | October 2022 | ||||
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| Publisher | Science Direct | Place of Publication | Editor | ||
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| Notes | MILAB | Approved | no | ||
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Admin @ si @ ANR2022 | Serial | 3763 | ||
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| Author | Juan Andrade; A. Sanfeliu | ||||
| Title | The effects of partial observability when building fully correlated maps | Type | Journal | ||
| Year | 2005 | Publication | IEEE Transactions on Robotics, 21(4):771–777 (IF: 1.486) | Abbreviated Journal | |
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Admin @ si @ AnS2005 | Serial | 594 | ||
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| Author | Juan Andrade; F. Thomas | ||||
| Title | Wire-Based Tracking using Mutual Information | Type | Miscellaneous | ||
| Year | 2006 | Publication | 10th International Symposium on Advances in Robot Kinematics, 3–14 | Abbreviated Journal | |
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| Address | Ljubljana (Slovenia) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ AnT2006 | Serial | 665 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu | ||||
| Title | Can biological solutions help computers detect symmetry? | Type | Conference Article | ||
| Year | 2017 | Publication | 40th European Conference on Visual Perception | Abbreviated Journal | |
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| Address | Berlin; Germany; August 2017 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT | Approved | no | ||
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Admin @ si @ APE2017 | Serial | 2995 | ||
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