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| Author | Isabel Guitart; Jordi Conesa; Luis Villarejo; Agata Lapedriza; David Masip; Antoni Perez; Elena Planas | ||||
| Title | Opinion Mining on Educational Resources at the Open University of Catalonia | Type | Conference Article | ||
| Year | 2013 | Publication | 3rd International Workshop on Adaptive Learning via Interactive, Collaborative and Emotional approaches. In conjunction with CISIS 2013: The 7th International Conference on Complex, Intelligent, and Software Intensive Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 385 - 390 | ||
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Abstract  |
In order to make improvements to teaching, it is vital to know what students think of the way they are taught. With that purpose in mind, exhaustively analyzing the forums associated with the subjects taught at the Universitat Oberta de Cataluya (UOC) would be extremely helpful, as the university's students often post comments on their learning experiences in them. Exploiting the content of such forums is not a simple undertaking. The volume of data involved is very large, and performing the task manually would require a great deal of effort from lecturers. As a first step to solve this problem, we propose a tool to automatically analyze the posts in forums of communities of UOC students and teachers, with a view to systematically mining the opinions they contain. This article defines the architecture of such tool and explains how lexical-semantic and language technology resources can be used to that end. For pilot testing purposes, the tool has been used to identify students' opinions on the UOC's Business Intelligence master's degree course during the last two years. The paper discusses the results of such test. The contribution of this paper is twofold. Firstly, it demonstrates the feasibility of using natural language parsing techniques to help teachers to make decisions. Secondly, it introduces a simple tool that can be refined and adapted to a virtual environment for the purpose in question. | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | 978-0-7695-4992-7 | Medium | ||
| Area | Expedition | Conference | ALICE | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | GCV2013 | Serial | 2268 | ||
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| Author | Sergio Vera; Debora Gil; Agnes Borras; Marius George Linguraru; Miguel Angel Gonzalez Ballester | ||||
| Title | Geometric Steerable Medial Maps | Type | Journal Article | ||
| Year | 2013 | Publication | Machine Vision and Applications | Abbreviated Journal | MVA |
| Volume | 24 | Issue | 6 | Pages | 1255-1266 |
| Keywords | Medial Representations ,Medial Manifolds Comparation , Surface , Reconstruction | ||||
| Abstract |
In order to provide more intuitive and easily interpretable representations of complex shapes/organs, medial manifolds should reach a compromise between simplicity in geometry and capability for restoring the anatomy/shape of the organ/volume. Existing morphological methods show excellent results when applied to 2D objects, but their quality drops across dimensions.
This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoids degenerated medial axis segments. Second, we introduce a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to syn- thetic shapes of known medial geometry. We also show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume. |
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Mubarak Shah | |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0932-8092 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; 605.203; 600.060; 600.044 | Approved | no | ||
| Call Number | IAM @ iam @ VGB2013 | Serial | 2192 | ||
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| Author | Cesar Isaza; Joaquin Salas; Bogdan Raducanu | ||||
| Title | Rendering ground truth data sets to detect shadows cast by static objects in outdoors | Type | Journal Article | ||
| Year | 2014 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 70 | Issue | 1 | Pages | 557-571 |
| Keywords | Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection | ||||
| Abstract |
In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ISR2014 | Serial | 2229 | ||
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| Author | Juan Ignacio Toledo; Alicia Fornes; Jordi Cucurull; Josep Llados | ||||
| Title | Election Tally Sheets Processing System | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 364-368 | ||
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| Abstract |
In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost. | ||||
| Address | Santorini; Greece; April 2016 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 602.006; 600.061; 601.225; 600.077; 600.097 | Approved | no | ||
| Call Number | TFC2016 | Serial | 2752 | ||
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| Author | Naila Murray | ||||
| Title | Predicting Saliency and Aesthetics in Images: A Bottom-up Perspective | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract |
In Part 1 of the thesis, we hypothesize that salient and non-salient image regions can be estimated to be the regions which are enhanced or assimilated in standard low-level color image representations. We prove this hypothesis by adapting a low-level model of color perception into a saliency estimation model. This model shares the three main steps found in many successful models for predicting attention in a scene: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. For such models, integrating spatial information and justifying the choice of various parameter values remain open problems. Our saliency model inherits a principled selection of parameters as well as an innate spatial pooling mechanism from the perception model on which it is based. This pooling mechanism has been fitted using psychophysical data acquired in color-luminance setting experiments. The proposed model outperforms the state-of-the-art at the task of predicting eye-fixations from two datasets. After demonstrating the effectiveness of our basic saliency model, we introduce an improved image representation, based on geometrical grouplets, that enhances complex low-level visual features such as corners and terminations, and suppresses relatively simpler features such as edges. With this improved image representation, the performance of our saliency model in predicting eye-fixations increases for both datasets.
In Part 2 of the thesis, we investigate the problem of aesthetic visual analysis. While a great deal of research has been conducted on hand-crafting image descriptors for aesthetics, little attention so far has been dedicated to the collection, annotation and distribution of ground truth data. Because image aesthetics is complex and subjective, existing datasets, which have few images and few annotations, have significant limitations. To address these limitations, we have introduced a new large-scale database for conducting Aesthetic Visual Analysis, which we call AVA. AVA contains more than 250,000 images, along with a rich variety of annotations. We investigate how the wealth of data in AVA can be used to tackle the challenge of understanding and assessing visual aesthetics by looking into several problems relevant for aesthetic analysis. We demonstrate that by leveraging the data in AVA, and using generic low-level features such as SIFT and color histograms, we can exceed state-of-the-art performance in aesthetic quality prediction tasks. Finally, we entertain the hypothesis that low-level visual information in our saliency model can also be used to predict visual aesthetics by capturing local image characteristics such as feature contrast, grouping and isolation, characteristics thought to be related to universal aesthetic laws. We use the weighted center-surround responses that form the basis of our saliency model to create a feature vector that describes aesthetics. We also introduce a novel color space for fine-grained color representation. We then demonstrate that the resultant features achieve state-of-the-art performance on aesthetic quality classification. As such, a promising contribution of this thesis is to show that several vision experiences – low-level color perception, visual saliency and visual aesthetics estimation – may be successfully modeled using a unified framework. This suggests a similar architecture in area V1 for both color perception and saliency and adds evidence to the hypothesis that visual aesthetics appreciation is driven in part by low-level cues. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu;Maria Vanrell | |
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Mur2012 | Serial | 2212 | ||
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| Author | Miquel Ferrer; I. Bardaji; Ernest Valveny; Dimosthenis Karatzas; Horst Bunke | ||||
| Title | Median Graph Computation by Means of Graph Embedding into Vector Spaces | Type | Book Chapter | ||
| Year | 2013 | Publication | Graph Embedding for Pattern Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | 45-72 | ||
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| Abstract |
In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant. | ||||
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| Publisher | Springer New York | Place of Publication | Editor | Yun Fu; Yungian Ma | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4614-4456-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ FBV2013 | Serial | 2421 | ||
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| Author | L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip | ||||
| Title | Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation | Type | Journal Article | ||
| Year | 2016 | Publication | Computers & Industrial Engineering | Abbreviated Journal | CIE |
| Volume | 94 | Issue | Pages | 93-104 | |
| Keywords | Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning | ||||
| Abstract |
In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one. | ||||
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| Publisher | PERGAMON-ELSEVIER SCIENCE LTD | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | CIE | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0360-8352 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR;MV; | Approved | no | ||
| Call Number | Admin @ si @ CFG2016 | Serial | 2749 | ||
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| Author | David Roche; Debora Gil; Jesus Giraldo | ||||
| Title | An inference model for analyzing termination conditions of Evolutionary Algorithms | Type | Conference Article | ||
| Year | 2011 | Publication | 14th Congrès Català en Intel·ligencia Artificial | Abbreviated Journal | |
| Volume | Issue | Pages | 216-225 | ||
| Keywords | Evolutionary Computation Convergence, Termination Conditions, Statistical Inference | ||||
| Abstract |
In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution. |
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| Address | Lleida, Catalonia (Spain) | ||||
| Corporate Author | Associació Catalana Intel·ligència Artificial | Thesis | |||
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| ISSN | ISBN | 978-1-60750-841-0 | Medium | ||
| Area | Expedition | Conference | CCIA | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ RGG2011a | Serial | 1677 | ||
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| Author | Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Human Head Pose Estimation on SASE database using Random Hough Regression Forests | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | 10165 | Issue | Pages | ||
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| Abstract |
In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests. | ||||
| Address | Cancun; Mexico; December 2016 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICPRW | ||
| Notes | HuPBA; | Approved | no | ||
| Call Number | Admin @ si @ LEA2016b | Serial | 2910 | ||
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| Author | David Augusto Rojas; Fahad Shahbaz Khan; Joost Van de Weijer | ||||
| Title | The Impact of Color on Bag-of-Words based Object Recognition | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1549–1553 | ||
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| Abstract |
In recent years several works have aimed at exploiting color information in order to improve the bag-of-words based image representation. There are two stages in which color information can be applied in the bag-of-words framework. Firstly, feature detection can be improved by choosing highly informative color-based regions. Secondly, feature description, typically focusing on shape, can be improved with a color description of the local patches. Although both approaches have been shown to improve results the combined merits have not yet been analyzed. Therefore, in this paper we investigate the combined contribution of color to both the feature detection and extraction stages. Experiments performed on two challenging data sets, namely Flower and Pascal VOC 2009; clearly demonstrate that incorporating color in both feature detection and extraction significantly improves the overall performance. | ||||
| Address | Istanbul (Turkey) | ||||
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| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | Approved | no | |||
| Call Number | CAT @ cat @ RKW2010 | Serial | 1415 | ||
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| Author | Xiangyang Li; Luis Herranz; Shuqiang Jiang | ||||
| Title | Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition | Type | Journal | ||
| Year | 2020 | Publication | ACM Transactions on Data Science | Abbreviated Journal | ACM |
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| Abstract |
In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks. | ||||
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| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LHJ2020 | Serial | 3423 | ||
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| Author | Smriti Joshi; Richard Osuala; Carlos Martin Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera | ||||
| Title | nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging | Type | Conference Article | ||
| Year | 2022 | Publication | International MICCAI Brainlesion Workshop | Abbreviated Journal | |
| Volume | 12963 | Issue | Pages | 540–551 | |
| Keywords | Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN | ||||
| Abstract |
In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ JOM2022 | Serial | 3800 | ||
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| Author | Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
| Title | Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions | Type | Conference Article | ||
| Year | 2019 | Publication | Visual Recognition for Medical Images workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 406-415 | ||
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| Abstract |
In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ AAF2019 | Serial | 3324 | ||
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| Author | Carlos Martin Isla; Victor M Campello; Cristian Izquierdo; Kaisar Kushibar; Carla Sendra Balcells; Polyxeni Gkontra; Alireza Sojoudi; Mitchell J Fulton; Tewodros Weldebirhan Arega; Kumaradevan Punithakumar; Lei Li; Xiaowu Sun; Yasmina Al Khalil; Di Liu; Sana Jabbar; Sandro Queiros; Francesco Galati; Moona Mazher; Zheyao Gao; Marcel Beetz; Lennart Tautz; Christoforos Galazis; Marta Varela; Markus Hullebrand; Vicente Grau; Xiahai Zhuang; Domenec Puig; Maria A Zuluaga; Hassan Mohy Ud Din; Dimitris Metaxas; Marcel Breeuwer; Rob J van der Geest; Michelle Noga; Stephanie Bricq; Mark E Rentschler; Andrea Guala; Steffen E Petersen; Sergio Escalera; Jose F Rodriguez Palomares; Karim Lekadir | ||||
| Title | Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Journal of Biomedical and Health Informatics | Abbreviated Journal | JBHI |
| Volume | 27 | Issue | 7 | Pages | 3302-3313 |
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| Abstract |
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ MCI2023 | Serial | 3880 | ||
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| Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
| Title | Face Presentation Attack Detection (PAD) Challenges | Type | Book Chapter | ||
| Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
| Volume | Issue | Pages | 17–35 | ||
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| Abstract |
In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ WGE2023b | Serial | 3956 | ||
| Permanent link to this record | |||||