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Author (up) 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
Volume 2652 Issue Pages 108-116
Keywords
Abstract ecture Notes in Computer Science 2652 108–116
Address Palma de Mallorca
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IbPRIA
Notes DAG;CIC Approved no
Call Number CAT @ cat @ BTL2003b Serial 369
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Author (up) 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
Keywords
Abstract
Address Funchal, Madeira (Portugal)
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
Notes DAG Approved no
Call Number DAG @ dag @ BoL2008 Serial 981
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Author (up) 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
Keywords
Abstract
Address Lisboa, Portugal
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 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 (up) 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 (up) 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
Keywords
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.
Address
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 CBMS
Notes MILAB; no proj Approved no
Call Number Admin @ si @ SBZ2021 Serial 3629
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Author (up) 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
Keywords
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.
Address Venice; Italy; October 2017
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 ICCV-PBDL
Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ AWG2017 Serial 2969
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Author (up) 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
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 LAMP Approved no
Call Number Admin @ si @ AWW2022 Serial 3781
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Author (up) Ajian Liu; Chenxu Zhao; Zitong Yu; Anyang Su; Xing Liu; Zijian Kong; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Guodong Guo
Title 3D High-Fidelity Mask Face Presentation Attack Detection Challenge Type Conference Article
Year 2021 Publication IEEE/CVF International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 814-823
Keywords
Abstract The threat of 3D mask to face recognition systems is increasing serious, and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of total amount of 54,600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask based attack detection. It attracted more than 200 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranked algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition.
Address Virtual; October 2021
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 ICCVW
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LZY2021 Serial 3646
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Author (up) Ajian Liu; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zichang Tan; Qi Yuan; Kai Wang; Chi Lin; Guodong Guo; Isabelle Guyon; Stan Z. Li
Title Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019 Type Conference Article
Year 2019 Publication IEEE International Conference on Computer Vision and Pattern Recognition-Workshop Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Anti-spoofing attack detection is critical to guarantee the security of face-based authentication and facial analysis systems. Recently, a multi-modal face anti-spoofing dataset, CASIA-SURF, has been released with the goal of boosting research in this important topic. CASIA-SURF is the largest public data set for facial anti-spoofing attack detection in terms of both, diversity and modalities: it comprises 1,000 subjects and 21,000 video samples. We organized a challenge around this novel resource to boost research in the subject. The Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.
Address California; June 2019
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 CVPRW
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ LWE2019 Serial 3329
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Author (up) Ajian Liu; Zichang Tan; Jun Wan; Sergio Escalera; Guodong Guo; Stan Z. Li
Title CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-Ethnicity Face Anti-Spoofing Type Conference Article
Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 1178-1186
Keywords
Abstract The issue of ethnic bias has proven to affect the performance of face recognition in previous works, while it still remains to be vacant in face anti-spoofing. Therefore, in order to study the ethnic bias for face anti-spoofing, we introduce the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, covering 3 ethnicities, 3 modalities, 1,607 subjects, and 2D plus 3D attack types. Five protocols are introduced to measure the affect under varied evaluation conditions, such as cross-ethnicity, unknown spoofs or both of them. As our knowledge, CASIA-SURF CeFA is the first dataset including explicit ethnic labels in current released datasets. Then, we propose a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias, which employs a partially shared fusion strategy to learn complementary information from multiple modalities. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability for other existing datasets, i.e., CASIA-SURF, OULU-NPU and SiW datasets. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2020?authuser=0.
Address Virtual; January 2021
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 WACV
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LTW2021 Serial 3661
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Author (up) Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez
Title Monocular Depth Estimation by Learning from Heterogeneous Datasets Type Conference Article
Year 2018 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal
Volume Issue Pages 2176 - 2181
Keywords
Abstract 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.
Address
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 IV
Notes ADAS; 600.124; 600.116; 600.118 Approved no
Call Number Admin @ si @ GUH2018 Serial 3183
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Author (up) Albert Andaluz; Francesc Carreras; Cristina Santa Marta;Debora Gil
Title Myocardial torsion estimation with Tagged-MRI in the OsiriX platform Type Conference Article
Year 2012 Publication ISBI Workshop on Open Source Medical Image Analysis software Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Myocardial torsion (MT) plays a crucial role in the assessment of the functionality of the
left ventricle. For this purpose, the IAM group at the CVC has developed the Harmonic Phase Flow (HPF) plugin for the Osirix DICOM platform . We have validated its funcionalty on sequences acquired using different protocols and including healthy and pathological cases. Results show similar torsion trends for SPAMM acquisitions, with pathological cases introducing expected deviations from the ground truth. Finally, we provide the plugin free of charge at http://iam.cvc.uab.es
Address Barcelona, Spain
Corporate Author Thesis
Publisher IEEE Place of Publication Editor Wiro Niessen (Erasmus MC) and Marc Modat (UCL)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ISBI
Notes IAM Approved no
Call Number IAM @ iam @ ACS2012 Serial 1900
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Author (up) Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
Title Banknote counterfeit detection through background texture printing analysis Type Conference Article
Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark.
Address Rumania; May 2016
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 DAS
Notes DAG; 600.061; 601.269; 600.097 Approved no
Call Number Admin @ si @ BRL2016 Serial 2950
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Author (up) Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
Title e-Counterfeit: a mobile-server platform for document counterfeit detection Type Conference Article
Year 2017 Publication 14th IAPR International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms.
Address Kyoto; Japan; November 2017
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 ICDAR
Notes DAG; 600.061; 600.097; 600.121 Approved no
Call Number Admin @ si @ BRL2018 Serial 3084
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Author (up) Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
Title Evaluation of Texture Descriptors for Validation of Counterfeit Documents Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1237-1242
Keywords
Abstract This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance.
Address
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 2379-2140 ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.061; 601.269; 600.097; 600.121 Approved no
Call Number Admin @ si @ BRL2017 Serial 3092
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