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Author | Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote | ||||
Title | MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | multi-view; cross-view; semantic segmentation; synthetic dataset | ||||
Abstract | We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . | ||||
Address | Bordeaux; France; October2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ AWW2022 | Serial | 3781 | ||
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Author | Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote | ||||
Title | Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB | Type | Conference Article | ||
Year | 2017 | Publication | 1st International Workshop on Physics Based Vision meets Deep Learning | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer.
Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset. |
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Address | Venice; Italy; October 2017 | ||||
Corporate Author | Thesis | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | ICCV-PBDL | ||
Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
Call Number | Admin @ si @ AWG2017 | Serial | 2969 | ||
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Author | Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer | ||||
Title | Self-supervised blur detection from synthetically blurred scenes | Type | Journal Article | ||
Year | 2019 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
Volume | 92 | Issue | Pages | 103804 | |
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Abstract | Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image. | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | LAMP; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ AGG2019 | Serial | 3301 | ||
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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 | ||
Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ Alv2022 | Serial | 3716 | ||
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Author | Ahmed Mounir Gad | ||||
Title | Object Localization Enhancement by Multiple Segmentation Fusion | Type | Report | ||
Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 152 | Issue | Pages | ||
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Corporate Author | Thesis | Master's thesis | |||
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Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ Mou2010 | Serial | 1346 | ||
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Author | Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Polyxeni Gkontra; Karim Lekadir; Gloria Menegaz; Petia Radeva | ||||
Title | A new scheme for the assessment of the robustness of Explainable Methods Applied to Brain Age estimation | Type | Conference Article | ||
Year | 2021 | Publication | 34th International Symposium on Computer-Based Medical Systems | Abbreviated Journal | |
Volume | Issue | Pages | 492-497 | ||
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Abstract | Deep learning methods show great promise in a range of settings including the biomedical field. Explainability of these models is important in these fields for building end-user trust and to facilitate their confident deployment. Although several Machine Learning Interpretability tools have been proposed so far, there is currently no recognized evaluation standard to transfer the explainability results into a quantitative score. Several measures have been proposed as proxies for quantitative assessment of explainability methods. However, the robustness of the list of significant features provided by the explainability methods has not been addressed. In this work, we propose a new proxy for assessing the robustness of the list of significant features provided by two explainability methods. Our validation is defined at functionality-grounded level based on the ranked correlation statistical index and demonstrates its successful application in the framework of brain aging estimation. We assessed our proxy to estimate brain age using neuroscience data. Our results indicate small variability and high robustness in the considered explainability methods using this new proxy. | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CBMS | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ SBZ2021 | Serial | 3629 | ||
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Author | Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva | ||||
Title | Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring | ||||
Abstract | Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. | ||||
Address | Bordeaux; France; October 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ SBC2022 | Serial | 3789 | ||
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Author | Agnes Borras; Josep Llados | ||||
Title | Object Image Retrieval by Shape Content in Complex Scenes Using Geometric Constraints | Type | Book Chapter | ||
Year | 2005 | Publication | Pattern Recognition And Image Analysis | Abbreviated Journal | LNCS |
Volume | 3522 | Issue | Pages | 325–332 | |
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Abstract | This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are repre- sented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method al- lows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling. | ||||
Address | Estoril (Portugal) | ||||
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Publisher | Springer Link | Place of Publication | Editor | ||
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Area | Expedition | Conference | |||
Notes | DAG; | Approved | no | ||
Call Number | DAG @ dag @ BoL2005; IAM @ iam @ BoL2005 | Serial | 556 | ||
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Author | Agnes Borras; Josep Llados | ||||
Title | Similarity-Based Object Retrieval Using Appearance and Geometric Feature Combination | Type | Book Chapter | ||
Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120 | Abbreviated Journal | LNCS |
Volume | 4478 | Issue | Pages | 33–39 | |
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Abstract | This work presents a content-based image retrieval system of general purpose that deals with cluttered scenes containing a given query object. The system is flexible enough to handle with a single image of an object despite its rotation, translation and scale variations. The image content is divided in parts that are described with a combination of features based on geometrical and color properties. The idea behind the feature combination is to benefit from a fuzzy similarity computation that provides robustness and tolerance to the retrieval process. The features can be independently computed and the image parts can be easily indexed by using a table structure on every feature value. Finally a process inspired in the alignment strategies is used to check the coherence of the object parts found in a scene. Our work presents a system of easy implementation that uses an open set of features and can suit a wide variety of applications. | ||||
Address | Girona (Spain) | ||||
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Publisher | Place of Publication | Editor | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-540-72848-1 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG; | Approved | no | ||
Call Number | DAG @ dag @ BoL2007a; IAM @ iam @ BoL2007a | Serial | 776 | ||
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Author | Agnes Borras; Josep Llados | ||||
Title | A Multi-Scale Layout Descriptor Based on Delaunay Triangulation for Image Retrieval | Type | Conference Article | ||
Year | 2008 | Publication | 3rd International Conference on Computer Vision Theory and Applications VISAPP (2) 2008 | Abbreviated Journal | |
Volume | 2 | Issue | Pages | 139-144 | |
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Address | Funchal, Madeira (Portugal) | ||||
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Area | Expedition | Conference | |||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ BoL2008 | Serial | 981 | ||
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Author | Agnes Borras; Josep Llados | ||||
Title | Corest: A measure of color and space stability to detect salient regions according to human criteria | Type | Conference Article | ||
Year | 2009 | Publication | 5th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
Volume | Issue | Pages | 204-209 | ||
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Address | Lisboa, Portugal | ||||
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ISSN | ISBN | 978-989-8111-69-2 | Medium | ||
Area | Expedition | Conference | VISAPP | ||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ BoL2009 | Serial | 1225 | ||
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Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell | ||||
Title | High-Level Clothes Description Based on Color-Texture and Structural Features | Type | Book Chapter | ||
Year | 2003 | Publication | Lecture Notes in Computer Science | Abbreviated Journal | |
Volume | 2652 | Issue | Pages | 108–116 | |
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Abstract | This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. | ||||
Address | Springer-Verlag | ||||
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Notes | DAG;CIC | Approved | no | ||
Call Number | CAT @ cat @ BTL2003a | Serial | 368 | ||
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Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell | ||||
Title | High-Level Clothes Description Based on Colour-Texture and Structural Features | Type | Conference Article | ||
Year | 2003 | Publication | 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 | Abbreviated Journal | |
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Address | Palma de Mallorca | ||||
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Notes | DAG;CIC | Approved | no | ||
Call Number | CAT @ cat @ BTL2003b | Serial | 369 | ||
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Author | Agnes Borras | ||||
Title | High-Level Clothes Description Based on Colour-Texture Features. | Type | Miscellaneous | ||
Year | 2002 | Publication | Director: J. Llados, Master Thesis. | Abbreviated Journal | |
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Notes | Approved | no | |||
Call Number | DAG @ dag @ Bor2002 | Serial | 322 | ||
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Author | Agnes Borras | ||||
Title | Contributions to the Content-Based Image Retrieval Using Pictorial Queries | Type | Book Whole | ||
Year | 2009 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | The broad access to digital cameras, personal computers and Internet, has lead to the generation of large volumes of data in digital form. If we want an effective usage of this huge amount of data, we need automatic tools to allow the retrieval of relevant information. Image data is a particular type of information that requires specific techniques of description and indexing. The computer vision field that studies these kind of techniques is called Content-Based Image Retrieval (CBIR). Instead of using text-based descriptions, a system of CBIR deals on properties that are inherent in the images themselves. Hence, the feature-based description provides a universal via of image expression in contrast with the more than 6000 languages spoken in the world.
Nowadays, the CBIR is a dynamic focus of research that has derived in important applications for many professional groups. The potential fields of application can be such diverse as: the medical domain, the crime prevention, the protection of the intel- lectual property, the journalism, the graphic design, the web search, the preservation of cultural heritage, etc. The definition on the role of the user is a key point in the development of a CBIR application. The user is in charge to formulate the queries from which the images are retrieved. We have centered our attention on the image retrieval techniques that use queries based on pictorial information. We have identified a taxonomy composed by four main query paradigms: query-by-selection, query-by-iconic-composition, query- by-sketch and query-by-paint. Each one of these paradigms allows a different degree of user expressivity. From a simple image selection, to a complete painting of the query, the user takes control of the input in the CBIR system. Along the chapters of this thesis we have analyzed the influence that each query paradigm imposes in the internal operations of a CBIR system. Moreover, we have proposed a set of contributions that we have exemplified in the context of a final application. |
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Address | Barcelona (Spain) | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Bellaterra | Editor | Josep Llados |
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Area | Expedition | Conference | |||
Notes | DAG; | Approved | no | ||
Call Number | DAG @ dag @ Bor2009; IAM @ iam @ Bor2009 | Serial | 1269 | ||
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