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| Author | Xose M. Pardo; Petia Radeva | ||||
| Title | Discriminant snakes for 3D reconstruction in medical Images. | Type | Conference Article | ||
| Year | 2000 | Publication | 15 th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | 4 | Issue | Pages  |
336-339 | |
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| Address | Barcelona. | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPR | ||
| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ PaR2000 | Serial | 234 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | Out-of-Sample Embedding by Sparse Representation | Type | Conference Article | ||
| Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
| Volume | 7626 | Issue | Pages |
336-344 | |
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| Abstract | A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-34165-6 | Medium | |
| Area | Expedition | Conference | SSPR&SPR | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RaD2012c | Serial | 2175 | ||
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| Author | Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados | ||||
| Title | Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis | Type | Conference Article | ||
| Year | 2022 | Publication | Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 | Abbreviated Journal | |
| Volume | 13424 | Issue | Pages |
336-348 | |
| Keywords | Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk | ||||
| Abstract | Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. | ||||
| Address | June 7-9, 2022, Las Palmas de Gran Canaria, Spain | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IGS | ||
| Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ BFC2022 | Serial | 3738 | ||
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| Author | Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; A. Rodriguez | ||||
| Title | Biometric Recognition Based on Line Shape Descriptors | Type | Conference Article | ||
| Year | 2005 | Publication | Sixth IAPR International Workshop on Graphics Recognition (GREC 2005) | Abbreviated Journal | |
| Volume | Issue | Pages |
335–344 | ||
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| Address | Hong Kong (China) | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ CSL2005 | Serial | 596 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 19 | Issue | 4 | Pages |
335-349 |
| Keywords | scene text; segmentation; detection; hierarchical grouping; perceptual organisation | ||||
| Abstract | Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective. Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art methods in unconstrained scenarios. |
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| Notes | DAG; 600.056; 601.197 | Approved | no | ||
| Call Number | Admin @ si @ GoK2016a | Serial | 2862 | ||
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| Author | Eduard Vazquez; Ramon Baldrich | ||||
| Title | Non-supervised goodness measure for image segmentation | Type | Conference Article | ||
| Year | 2010 | Publication | Proceedings of The CREATE 2010 Conference | Abbreviated Journal | |
| Volume | Issue | Pages |
334–335 | ||
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| Address | Gjovik, Norway | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CREATE | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ VaB2010 | Serial | 1299 | ||
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| Author | Debora Gil; Agnes Borras; Sergio Vera; Miguel Angel Gonzalez Ballester | ||||
| Title | A Validation Benchmark for Assessment of Medial Surface Quality for Medical Applications | Type | Conference Article | ||
| Year | 2013 | Publication | 9th International Conference on Computer Vision Systems | Abbreviated Journal | |
| Volume | 7963 | Issue | Pages |
334-343 | |
| Keywords | Medial Surfaces; Shape Representation; Medical Applications; Performance Evaluation | ||||
| Abstract | Confident use of medial surfaces in medical decision support systems requires evaluating their quality for detecting pathological deformations and describing anatomical volumes. Validation in the medical imaging field is a challenging task mainly due to the difficulties for getting consensual ground truth. In this paper we propose a validation benchmark for assessing medial surfaces in the context of medical applications. Our benchmark includes a home-made database of synthetic medial surfaces and volumes and specific scores for evaluating surface accuracy, its stability against volume deformations and its capabilities for accurate reconstruction of anatomical volumes. | ||||
| Address | Sant Petersburg; Russia; July 2013 | ||||
| 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 | 978-3-642-39401-0 | Medium | |
| Area | Expedition | Conference | ICVS | ||
| Notes | IAM; 600.044; 600.060 | Approved | no | ||
| Call Number | Admin @ si @ GBV2013 | Serial | 2300 | ||
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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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| Series Volume | Series Issue | Edition | |||
| 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 | ||
| Language | Summary Language | Original Title | |||
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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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| 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 | ||||
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| 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 | 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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