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Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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Title |
A new image centrality descriptor for wrinkle frame detection in WCE videos |
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2013 |
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13th IAPR Conference on Machine Vision Applications |
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Small bowel motility dysfunctions are a widespread functional disorder characterized by abdominal pain and altered bowel habits in the absence of specific and unique organic pathology. Current methods of diagnosis are complex and can only be conducted at some highly specialized referral centers. Wireless Video Capsule Endoscopy (WCE) could be an interesting diagnostic alternative that presents excellent clinical advantages, since it is non-invasive and can be conducted by non specialists. The purpose of this work is to present a new method for the detection of wrinkle frames in WCE, a critical characteristic to detect one of the main motility events: contractions. The method goes beyond the use of one of the classical image feature, the Histogram |
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Kyoto; Japan; May 2013 |
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MVA |
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OR; MILAB; 600.046;MV |
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Admin @ si @ SDZ2013 |
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2239 |
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Victor Borjas; Jordi Vitria; Petia Radeva |
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Title |
Gradient Histogram Background Modeling for People Detection in Stationary Camera Environments |
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2013 |
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13th IAPR Conference on Machine Vision Applications |
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Best Poster AwardOne of the big challenges of today person detectors is the decreasing of the false positive rate. In this paper, we propose a novel framework to customize person detectors in static camera scenarios in order to reduce this rate. This scheme includes background modeling for subtraction based on gradient histograms and Mean-Shift clustering. Our experiments show that the detection improved compared to using only the output from the pedestrian detector reducing 87% of the false positives and therefore the overall precision of the detection
was increased signi cantly. |
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Kyoto; Japan; May 2013 |
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OR; MILAB;MV |
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BVR2013 |
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2238 |
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David Berga; Xavier Otazu |
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Title |
Computations of top-down attention by modulating V1 dynamics |
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2020 |
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Computational and Mathematical Models in Vision |
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St. Pete Beach; Florida; May 2020 |
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MODVIS |
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NEUROBIT |
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Admin @ si @ BeO2020a |
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3376 |
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Christian Keilstrup Ingwersen; Artur Xarles; Albert Clapes; Meysam Madadi; Janus Nortoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera |
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Title |
Video-based Skill Assessment for Golf: Estimating Golf Handicap |
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Conference Article |
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2023 |
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Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
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31-39 |
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Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf. |
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Otawa; Canada; October 2023 |
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MMSports |
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HUPBA |
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Admin @ si @ KXC2023 |
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3929 |
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Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes |
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Title |
ASTRA: An Action Spotting TRAnsformer for Soccer Videos |
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2023 |
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Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
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93–102 |
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In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. |
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Otawa; Canada; October 2023 |
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MMSports |
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HUPBA |
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Admin @ si @ XEM2023 |
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3970 |
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Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus |
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Title |
Mobile eHealth Platform for Home Monitoring of Bipolar Disorder |
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Conference Article |
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Year |
2021 |
Publication |
27th ACM International Conference on Multimedia Modeling |
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12573 |
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330-341 |
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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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LNCS |
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MMM |
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HUPBA; no proj |
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Admin @ si @ CEE2021 |
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3659 |
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Author |
Petia Radeva; A.Amini; J.Huang; Enric Marti |
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Title |
Deformable B-Solids and Implicit Snakes for Localization and Tracking of SPAMM MRI-Data |
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Conference Article |
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1996 |
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Workshop on Mathematical Methods in Biomedical Image Analysis |
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192-201 |
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To date, MRI-SPAMM data from different image slices have been analyzed independently. In this paper, we propose an approach for 3D tag localization and tracking of SPAMM data by a novel deformable B-solid. The solid is defined in terms of a 3D tensor product B-spline. The isoparametric curves of the B-spline solid have special importance. These are termed implicit snakes as they deform under image forces from tag lines in different image slices. The localization and tracking of tag lines is performed under constraints of continuity and smoothness of the B-solid. The framework unifies the problems of localization, and displacement fitting and interpolation into the same procedure utilizing B-spline bases for interpolation. To track motion from boundaries and restrict image forces to the myocardium, a volumetric model is employed as a pair of coupled endocardial and epicardial B-spline surfaces. To recover deformations in the LV an energy-minimization problem is posed where both tag and ... |
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San Francisco CA |
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IEEE Computer Society |
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0-8186-7368-0 |
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MMBIA ’96 |
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MILAB;IAM; |
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IAM @ iam @ RAH1996 |
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1630 |
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Michal Drozdzal; Laura Igual; Petia Radeva; Jordi Vitria; C. Malagelada; Fernando Azpiroz |
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Title |
Aligning Endoluminal Scene Sequences in Wireless Capsule Endoscopy |
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2010 |
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IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis |
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117–124 |
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Intestinal motility analysis is an important examination in detection of various intestinal malfunctions. One of the big challenges of automatic motility analysis is how to compare sequence of images and extract dynamic paterns taking into account the high deformability of the intestine wall as well as the capsule motion. From clinical point of view the ability to align endoluminal scene sequences will help to find regions of similar intestinal activity and in this way will provide a valuable information on intestinal motility problems. This work, for first time, addresses the problem of aligning endoluminal sequences taking into account motion and structure of the intestine. To describe motility in the sequence, we propose different descriptors based on the Sift Flow algorithm, namely: (1) Histograms of Sift Flow Directions to describe the flow course, (2) Sift Descriptors to represent image intestine structure and (3) Sift Flow Magnitude to quantify intestine deformation. We show that the merge of all three descriptors provides robust information on sequence description in terms of motility. Moreover, we develop a novel methodology to rank the intestinal sequences based on the expert feedback about relevance of the results. The experimental results show that the selected descriptors are useful in the alignment and similarity description and the proposed method allows the analysis of the WCE. |
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San Francisco; CA; USA; June 2010 |
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2160-7508 |
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978-1-4244-7029-7 |
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MMBIA |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DIR2010 |
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1316 |
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Danna Xue; Fei Yang; Pei Wang; Luis Herranz; Jinqiu Sun; Yu Zhu; Yanning Zhang |
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SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision |
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2022 |
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30th ACM International Conference on Multimedia |
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6539-6548 |
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Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework. |
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Lisboa, Portugal, October 2022 |
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Association for Computing Machinery |
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978-1-4503-9203-7 |
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MM |
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MACO; 600.161; 601.400 |
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Admin @ si @ XYW2022 |
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3758 |
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Siyang Song; Micol Spitale; Cheng Luo; German Barquero; Cristina Palmero; Sergio Escalera; Michel Valstar; Tobias Baur; Fabien Ringeval; Elisabeth Andre; Hatice Gunes |
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Title |
REACT2023: The First Multiple Appropriate Facial Reaction Generation Challenge |
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Conference Article |
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2023 |
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Proceedings of the 31st ACM International Conference on Multimedia |
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9620–9624 |
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The Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions. The goal of the challenge is to provide the first benchmark test set for multi-modal information processing and to foster collaboration among the audio, visual, and audio-visual behaviour analysis and behaviour generation (a.k.a generative AI) communities, to compare the relative merits of the approaches to automatic appropriate facial reaction generation under different spontaneous dyadic interaction conditions. This paper presents: (i) the novelties, contributions and guidelines of the REACT2023 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of the baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2023. |
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Otawa; Canada; October 2023 |
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HUPBA |
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Admin @ si @ SSL2023 |
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3931 |
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Marta Nuñez-Garcia; Sonja Simpraga; M.Angeles Jurado; Maite Garolera; Roser Pueyo; Laura Igual |
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FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization |
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2015 |
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Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015 |
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Munich; Germany; October 2015 |
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MILAB |
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Admin @ si @ NSJ2015 |
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2674 |
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Mohammad ali Bagheri; Qigang Gao; Sergio Escalera |
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Three-Dimensional Design of Error Correcting Output Codes |
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2012 |
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8th International Conference on Machine Learning and Data Mining |
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Berlin, Germany |
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MLDM |
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HuPBA;MILAB |
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Admin @ si @ BGE2012a |
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2041 |
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Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva |
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Efficient automatic segmentation of vessels |
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2012 |
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16th Conference on Medical Image Understanding and Analysis |
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Swansea, United Kingdom |
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Area |
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Expedition |
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Conference |
MIUA |
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Notes |
MILAB |
Approved |
no |
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Call Number |
Admin @ si @ |
Serial |
2137 |
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| Permanent link to this record |
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Author |
Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
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Title |
Conditional Random Fields for image segmentation in Intravascular Ultrasound |
Type |
Conference Article |
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Year |
2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
13–14 |
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Keywords |
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Abstract |
We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved. |
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Address |
Girona |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
MICCAT |
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Notes |
MILAB;HUPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ CPF2010 |
Serial |
1453 |
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| Permanent link to this record |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Classyfing Agitation in Sedated ICU Patients |
Type |
Conference Article |
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Year |
2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
19–20 |
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Keywords |
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Abstract |
Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances. |
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Address |
Girona |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
MICCAT |
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Notes |
MILAB;HUPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ COR2010 |
Serial |
1467 |
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| Permanent link to this record |
