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| Author | Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
| Title | Monitoring war destruction from space using machine learning | Type | Journal Article | ||
| Year | 2021 | Publication | Proceedings of the National Academy of Sciences of the United States of America | Abbreviated Journal | PNAS |
| Volume | 118 | Issue | 23 | Pages | e2025400118 |
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| Abstract | 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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| Notes | ADAS; 600.118 | Approved | no | ||
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Admin @ si @ MGH2021 | Serial | 3584 | ||
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| Author | Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
| Title | Monitoring War Destruction from Space: A Machine Learning Approach | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | 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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| Notes | ADAS; 600.118 | Approved | no | ||
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Admin @ si @ MGH2020 | Serial | 3489 | ||
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| Author | Patricia Marquez; Debora Gil; Aura Hernandez-Sabate | ||||
| Title | Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars | Abbreviated Journal | |
| Volume | Issue | Pages | 624-631 | ||
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| Abstract | Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Area | Expedition | Conference | CVTT:E2M | ||
| Notes | IAM; ADAS; 600.044; 600.057; 601.145 | Approved | no | ||
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Admin @ si @ MGH2013b | Serial | 2351 | ||
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| Author | Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda | ||||
| Title | ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos | Type | Miscellaneous | ||
| Year | 2014 | Publication | 8th International Congress on University Teaching and Innovation | Abbreviated Journal | |
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| Address | Tarragona; juliol 2014 | ||||
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| Area | Expedition | Conference | CIDUI | ||
| Notes | IAM; ADAS; 600.076; 600.063; 600.075 | Approved | no | ||
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Admin @ si @ MGG2014 | Serial | 2457 | ||
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| Author | Pedro Martins; Carlo Gatta; Paulo Carvalho | ||||
| Title | Feature-driven Maximally Stable Extremal Regions | Type | Conference Article | ||
| Year | 2012 | Publication | 7th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 490-497 | ||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MILAB | Approved | no | ||
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Admin @ si @ MGC2012 | Serial | 2139 | ||
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| Author | H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena | ||||
| Title | Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior | Type | Journal Article | ||
| Year | 2016 | Publication | Patter Recognition Letters | Abbreviated Journal | PRL |
| Volume | 76 | Issue | 1 | Pages | 76-82 |
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| Notes | IAM; 600.060 | Approved | no | ||
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Admin @ si @ MFV2017b | Serial | 2941 | ||
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| Author | H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil | ||||
| Title | Medial structure generation for registration of anatomical structures | Type | Book Chapter | ||
| Year | 2017 | Publication | Skeletonization, Theory, Methods and Applications | Abbreviated Journal | |
| Volume | 11 | Issue | Pages | ||
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| Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
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Admin @ si @ MFV2017a | Serial | 2935 | ||
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| Author | Neelu Madan; Arya Farkhondeh; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund | ||||
| Title | Temporal Cues From Socially Unacceptable Trajectories for Anomaly Detection | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2150-2158 | ||
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| Abstract | State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa. | ||||
| Address | Virtual; October 2021 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
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Admin @ si @ MFN2021 | Serial | 3649 | ||
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| Author | Joan Mas; Alicia Fornes; Josep Llados | ||||
| Title | An Interactive Transcription System of Census Records using Word-Spotting based Information Transfer | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 54-59 | ||
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| Abstract | This paper presents a system to assist in the transcription of historical handwritten census records in a crowdsourcing platform. Census records have a tabular structured layout. They consist in a sequence of rows with information of homes ordered by street address. For each household snippet in the page, the list of family members is reported. The censuses are recorded in intervals of a few years and the information of individuals in each household is quite stable from a point in time to the next one. This redundancy is used to assist the transcriber, so the redundant information is transferred from the census already transcribed to the next one. Household records are aligned from one year to the next one using the knowledge of the ordering by street address. Given an already transcribed census, a query by string word spotting is applied. Thus, names from the census in time t are used as queries in the corresponding home record in time t+1. Since the search is constrained, the obtained precision-recall values are very high, with an important reduction in the transcription time. The proposed system has been tested in a real citizen-science experience where non expert users transcribe the census data of their home town. | ||||
| Address | Santorini; Greece; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 603.053; 602.006; 600.061; 600.077; 600.097 | Approved | no | ||
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Admin @ si @ MFL2016 | Serial | 2751 | ||
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| Author | Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang | ||||
| Title | Historical Cryptology | Type | Book Chapter | ||
| Year | 2024 | Publication | Learning and Experiencing Cryptography with CrypTool and SageMath | Abbreviated Journal | |
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| Abstract | Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. | ||||
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| Notes | DAG | Approved | no | ||
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Admin @ si @ MFK2024 | Serial | 4020 | ||
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| Author | Armin Mehri; Angel Sappa | ||||
| Title | Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition-Workshops | Abbreviated Journal | |
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| Abstract | This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art | ||||
| Address | Long beach; California; USA; June 2019 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
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Admin @ si @ MeS2019 | Serial | 3271 | ||
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| Author | Armin Mehri | ||||
| Title | Deep learning based architectures for cross-domain image processing | Type | Book Whole | ||
| Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Human vision is restricted to the visual-optical spectrum. Machine vision is not.
Cameras sensitive to diverse infrared spectral bands can improve the capacities of autonomous systems and provide a comprehensive view. Relevant scene content can be made visible, particularly in situations when sensors of other modalities, such as a visual-optical camera, require a source of illumination. As a result, increasing the level of automation not only avoids human errors but also reduces machine-induced errors. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can conceivably increase the robustness of many autonomous systems. Robotics, automobiles, biometrics, security, surveillance, and the military are some examples of fields that can profit from the use of infrared imagery in their respective applications. Although multimodal spectral sensors have come a long way, there are still several bottlenecks that prevent us from combining their output information and using them as comprehensive images. The primary issue with infrared imaging is the lack of potential benefits due to their cost influence on sensor resolution, which grows exponentially with greater resolution. Due to the more costly sensor technology required for their development, their resolutions are substantially lower than thoseof regular digital cameras. This thesis aims to improve beyond-visible-spectrum machine vision by integrating multi-modal spectral sensors. The emphasis is on transforming the produced images to enhance their resolution to match expected human perception, bring the color representation close to human understanding of natural color, and improve machine vision application performance. This research focuses mainly on two tasks, image Colorization and Image Super resolution for both single- and cross-domain problems. We first start with an extensive review of the state of the art in both tasks, point out the shortcomings of existing approaches, and then present our solutions to address their limitations. Our solutions demonstrate that low-cost channel information (i.e., visible image) can be used to improve expensive channel information (i.e., infrared image), resulting in images with higher quality and closer to human perception at a lower cost than a high-cost infrared camera. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Angel Sappa | |
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| ISSN | ISBN | 978-84-126409-1-5 | Medium | ||
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| Notes | MSIAU | Approved | no | ||
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Admin @ si @ Meh2023 | Serial | 3959 | ||
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| Author | Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras | ||||
| Title | Multi-part body segmentation based on depth maps for soft biometry analysis | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 56 | Issue | Pages | 14-21 | |
| Keywords | 3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis | ||||
| Abstract | This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data. | ||||
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| Notes | HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB | Approved | no | ||
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Admin @ si @ MEG2015 | Serial | 2588 | ||
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| Author | Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl | ||||
| Title | Decryption of historical manuscripts: the DECRYPT project | Type | Journal Article | ||
| Year | 2020 | Publication | Cryptologia | Abbreviated Journal | CRYPT |
| Volume | 44 | Issue | 6 | Pages | 545-559 |
| Keywords | automatic decryption; cipher collection; historical cryptology; image transcription | ||||
| Abstract | Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology. | ||||
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| Notes | DAG; 600.140; 600.121 | Approved | no | ||
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Admin @ si @ MEF2020 | Serial | 3347 | ||
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| Author | Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez | ||||
| Title | Top-down model fitting for hand pose recovery in sequences of depth images | Type | Journal Article | ||
| Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 79 | Issue | Pages | 63-75 | |
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| Abstract | State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs. | ||||
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| Notes | HUPBA; 600.098 | Approved | no | ||
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Admin @ si @ MEC2018 | Serial | 3203 | ||
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