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Author | E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva | ||||
Title | Regularized Clustering for Egocentric Video Segmentation | Type | Book Chapter | ||
Year | 2015 | Publication | Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
327-336 | ||
Keywords | Temporal video segmentation ; Egocentric videos ; Clustering | ||||
Abstract | In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. | ||||
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Publisher | Springer International Publishing | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-319-19390-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @TDB2015a | Serial | 2781 | ||
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Author | Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo | ||||
Title | Subspace Procrustes Analysis | Type | Journal Article | ||
Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 121 | Issue | 3 | Pages ![]() |
327–343 |
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Abstract | Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach. |
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Notes | MILAB; HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ PTI2017 | Serial | 2841 | ||
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Author | Joan Serrat; Felipe Lumbreras; Idoia Ruiz | ||||
Title | Learning to measure for preshipment garment sizing | Type | Journal Article | ||
Year | 2018 | Publication | Measurement | Abbreviated Journal | MEASURE |
Volume | 130 | Issue | Pages ![]() |
327-339 | |
Keywords | Apparel; Computer vision; Structured prediction; Regression | ||||
Abstract | Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | ADAS; MSIAU; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SLR2018 | Serial | 3128 | ||
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Author | Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
Title | Automatic Verification of Properly Signed Multi-page Document Images | Type | Conference Article | ||
Year | 2015 | Publication | Proceedings of the Eleventh International Symposium on Visual Computing | Abbreviated Journal | |
Volume | 9475 | Issue | Pages ![]() |
327-336 | |
Keywords | Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow | ||||
Abstract | In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. | ||||
Address | Las Vegas, Nevada, USA; December 2015 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | 9475 | Series Issue | Edition | ||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ISVC | ||
Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3189 | ||
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Author | Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio | ||||
Title | Deriving global quantitative tumor response parameters from 18F-FDG PET-CT scans in patients with non-Hodgkins lymphoma | Type | Journal Article | ||
Year | 2015 | Publication | Nuclear Medicine Communications | Abbreviated Journal | NMC |
Volume | 36 | Issue | 4 | Pages ![]() |
328-333 |
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Abstract | OBJECTIVES:
The aim of the study was to address the need for quantifying the global cancer time evolution magnitude from a pair of time-consecutive positron emission tomography-computed tomography (PET-CT) scans. In particular, we focus on the computation of indicators using image-processing techniques that seek to model non-Hodgkin's lymphoma (NHL) progression or response severity. MATERIALS AND METHODS: A total of 89 pairs of time-consecutive PET-CT scans from NHL patients were stored in a nuclear medicine station for subsequent analysis. These were classified by a consensus of nuclear medicine physicians into progressions, partial responses, mixed responses, complete responses, and relapses. The cases of each group were ordered by magnitude following visual analysis. Thereafter, a set of quantitative indicators designed to model the cancer evolution magnitude within each group were computed using semiautomatic and automatic image-processing techniques. Performance evaluation of the proposed indicators was measured by a correlation analysis with the expert-based visual analysis. RESULTS: The set of proposed indicators achieved Pearson's correlation results in each group with respect to the expert-based visual analysis: 80.2% in progressions, 77.1% in partial response, 68.3% in mixed response, 88.5% in complete response, and 100% in relapse. In the progression and mixed response groups, the proposed indicators outperformed the common indicators used in clinical practice [changes in metabolic tumor volume, mean, maximum, peak standardized uptake value (SUV mean, SUV max, SUV peak), and total lesion glycolysis] by more than 40%. CONCLUSION: Computing global indicators of NHL response using PET-CT imaging techniques offers a strong correlation with the associated expert-based visual analysis, motivating the future incorporation of such quantitative and highly observer-independent indicators in oncological decision making or treatment response evaluation scenarios. |
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ SDE2015 | Serial | 2605 | ||
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Author | Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto | ||||
Title | Va-ID: A trust-based virtual assessment system | Type | Conference Article | ||
Year | 2014 | Publication | 6th International Conference on Intelligent Networking and Collaborative Systems | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
328 - 335 | ||
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Abstract | Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model. | ||||
Address | Salerna; Italy; September 2014 | ||||
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ISSN | ISBN | 978-1-4799-6386-7 | Medium | ||
Area | Expedition | Conference | INCOS | ||
Notes | OR; HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ ABB2014 | Serial | 2620 | ||
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Author | Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell | ||||
Title | Who Painted this Painting? | Type | Conference Article | ||
Year | 2010 | Publication | Proceedings of The CREATE 2010 Conference | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
329–333 | ||
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Address | Gjovik (Norway) | ||||
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Area | Expedition | Conference | CREATE | ||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ KWV2010 | Serial | 1329 | ||
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Author | Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai | ||||
Title | Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks | Type | Conference Article | ||
Year | 2022 | Publication | 17th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | 13804 | Issue | Pages ![]() |
329–344 | |
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Abstract | Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-031-25068-2 | Medium | ||
Area | Expedition | Conference | ECCV-TiE | ||
Notes | DAG; 600.162; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ GBC2022 | Serial | 3795 | ||
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Author | Oscar Camara; Estanislao Oubel; Gemma Piella; Simone Balocco; Mathieu De Craene; Alejandro F. Frangi | ||||
Title | Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging | Type | Conference Article | ||
Year | 2009 | Publication | 5th International Conference on Functional Imaging and Modeling of the Heart | Abbreviated Journal | |
Volume | 5528 | Issue | Pages ![]() |
330–338 | |
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Abstract | In this work, we present a registration framework for cardiac cine MRI (cMRI), tagged (tMRI) and delay-enhancement MRI (deMRI), where the two main issues to find an accurate alignment between these images have been taking into account: the presence of tags in tMRI and respiration artifacts in all sequences. A steerable pyramid image decomposition has been used for detagging purposes since it is suitable to extract high-order oriented structures by directional adaptive filtering. Shift correction of cMRI is achieved by firstly maximizing the similarity between the Long Axis and Short Axis cMRI. Subsequently, these shift-corrected images are used as target images in a rigid registration procedure with their corresponding tMRI/deMRI in order to correct their shift. The proposed registration framework has been evaluated by 840 registration tests, considerably improving the alignment of the MR images (mean RMS error of 2.04mm vs. 5.44mm). | ||||
Address | Nice, France | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-01931-9 | Medium | |
Area | Expedition | Conference | FIMH | ||
Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ COP2009 | Serial | 1255 | ||
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Author | Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus | ||||
Title | Mobile eHealth Platform for Home Monitoring of Bipolar Disorder | Type | Conference Article | ||
Year | 2021 | Publication | 27th ACM International Conference on Multimedia Modeling | Abbreviated Journal | |
Volume | 12573 | Issue | Pages ![]() |
330-341 | |
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Abstract | People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.
MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians. |
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | MMM | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CEE2021 | Serial | 3659 | ||
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Author | Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
Title | Using the Average Landmark Vector Method for Robot Homing | Type | Conference Article | ||
Year | 2007 | Publication | Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA | Abbreviated Journal | |
Volume | 163 | Issue | Pages ![]() |
331–338 | |
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ISSN | ISBN | 978–1–58603–798–7 | Medium | ||
Area | Expedition | Conference | CCIA’07 | ||
Notes | RV;ADAS | Approved | no | ||
Call Number | Admin @ si @ GRL2007 | Serial | 899 | ||
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Author | Joan Oliver; Ricardo Toledo; J. Pujol; J. Sorribes; E. Valderrama | ||||
Title | Un ABP basado en la robotica para las ingenierias informaticas | Type | Miscellaneous | ||
Year | 2009 | Publication | 15th Jornadas de Enseñanza Universitaria de la Informatica | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
331–338 | ||
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Address | Barcelona, Spain | ||||
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ISSN | ISBN | ISBN:978–84–692–2758–9 | Medium | ||
Area | Expedition | Conference | JENUI | ||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ OTP2009 | Serial | 1253 | ||
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Author | Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados | ||||
Title | Multimodal page classification in administrative document image streams | Type | Journal Article | ||
Year | 2014 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
Volume | 17 | Issue | 4 | Pages ![]() |
331-341 |
Keywords | Digital mail room; Multimodal page classification; Visual and textual document description | ||||
Abstract | In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 1433-2833 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079 | Approved | no | ||
Call Number | Admin @ si @ RFK2014 | Serial | 2523 | ||
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Author | Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate | ||||
Title | Feature Extraction by Using Dual-Generalized Discriminative Common Vectors | Type | Journal Article | ||
Year | 2019 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
Volume | 61 | Issue | 3 | Pages ![]() |
331-351 |
Keywords | Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning | ||||
Abstract | In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods. | ||||
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Notes | DAG; ADAS; 600.084; 600.118; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DRR2019 | Serial | 3172 | ||
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Author | Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov | ||||
Title | Variable Rate Deep Image Compression with Modulated Autoencoder | Type | Journal Article | ||
Year | 2020 | Publication | IEEE Signal Processing Letters | Abbreviated Journal | SPL |
Volume | 27 | Issue | Pages ![]() |
331-335 | |
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Abstract | Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters. | ||||
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Notes | LAMP; ADAS; 600.141; 600.120; 600.118 | Approved | no | ||
Call Number | Admin @ si @ YHW2020 | Serial | 3346 | ||
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