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Author  |
P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa | ||||
| Title | Feature Point Descriptors: Infrared and Visible Spectra | Type | Journal Article | ||
| Year | 2014 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 14 | Issue | 2 | Pages | 3690-3701 |
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| Abstract | This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. | ||||
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| Notes | ADAS;600.055; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ RCA2014a | Serial | 2474 | ||
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| Author |
P. Canals; Simone Balocco; O. Diaz; J. Li; A. Garcia Tornel; M. Olive Gadea; M. Ribo | ||||
| Title | A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning | Type | Journal Article | ||
| Year | 2023 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 104 | Issue | 102170 | Pages | |
| Keywords | Artificial intelligence; Deep learning; Stroke; Thrombectomy; Vascular feature extraction; Vascular tortuosity | ||||
| Abstract | Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients. | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ CBD2023 | Serial | 4005 | ||
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| Author |
P. Andreeva; Maya Dimitrova; Petia Radeva | ||||
| Title | Data Mining Learning Models and Algorithms for Medical Applications | Type | Book Chapter | ||
| Year | 2004 | Publication | 18 Conference Systems for Automation of Engineering and Research (SEAR 2004) | Abbreviated Journal | |
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| Address | Varna (Bulgaria) | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ ADR2004 | Serial | 474 | ||
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| Author |
Ozge Mercanoglu Sincan; Julio C. S. Jacques Junior; Sergio Escalera; Hacer Yalim Keles | ||||
| Title | ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research | Type | Conference Article | ||
| Year | 2021 | Publication | Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 3467-3476 | ||
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| Abstract | The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories. | ||||
| Address | Virtual; June 2021 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ MJE2021 | Serial | 3560 | ||
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| Author |
Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer | ||||
| Title | Does Multimodality Help Human and Machine for Translation and Image Captioning? | Type | Conference Article | ||
| Year | 2016 | Publication | 1st conference on machine translation | Abbreviated Journal | |
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| Abstract | This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR. | ||||
| Address | Berlin; Germany; August 2016 | ||||
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| Area | Expedition | Conference | WMT | ||
| Notes | LAMP; 600.106 ; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ CAW2016 | Serial | 2761 | ||
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| Author |
Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer | ||||
| Title | LIUM-CVC Submissions for WMT17 Multimodal Translation Task | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd Conference on Machine Translation | Abbreviated Journal | |
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| Abstract | This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU. | ||||
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| Area | Expedition | Conference | WMT | ||
| Notes | LAMP; 600.106; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ CAB2017 | Serial | 3035 | ||
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| Author |
Ozan Caglayan; Adrien Bardet; Fethi Bougares; Loic Barrault; Kai Wang; Marc Masana; Luis Herranz; Joost Van de Weijer | ||||
| Title | LIUM-CVC Submissions for WMT18 Multimodal Translation Task | Type | Conference Article | ||
| Year | 2018 | Publication | 3rd Conference on Machine Translation | Abbreviated Journal | |
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| Abstract | This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previou multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions
ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR. |
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| Address | Brussels; Belgium; October 2018 | ||||
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| Area | Expedition | Conference | WMT | ||
| Notes | LAMP; 600.106; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ CBB2018 | Serial | 3240 | ||
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| Author |
Oualid M. Benkarim; Petia Radeva; Laura Igual | ||||
| Title | Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation | Type | Conference Article | ||
| Year | 2014 | Publication | 8th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 8563 | Issue | Pages | 138-147 | |
| Keywords | MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation | ||||
| Abstract | The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset. The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems. |
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| Address | Palma de Mallorca; July 2014 | ||||
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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 | 0302-9743 | ISBN | 978-3-319-08848-8 | Medium | |
| Area | Expedition | Conference | AMDO | ||
| Notes | MILAB; OR | Approved | no | ||
| Call Number | Admin @ si @ BRI2014 | Serial | 2494 | ||
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| Author |
Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez | ||||
| Title | Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Systems, Man and Cybernetics (Part B) | Abbreviated Journal | TSMCB |
| Volume | 44 | Issue | 12 | Pages | 2379-2390 |
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| Abstract | The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. | ||||
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| ISSN | 2168-2267 | ISBN | Medium | ||
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| Notes | HuPBA; ISE; 600.078;MILAB | Approved | no | ||
| Call Number | Admin @ si @ LRE2014 | Serial | 2442 | ||
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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 |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras | ||||
| Title | Segmentation of aerial images for plausible detail synthesis | Type | Journal Article | ||
| Year | 2018 | Publication | Computers & Graphics | Abbreviated Journal | CG |
| Volume | 71 | Issue | Pages | 23-34 | |
| Keywords | Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation | ||||
| Abstract | The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. | ||||
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| ISSN | 0097-8493 | ISBN | Medium | ||
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| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ACC2018 | Serial | 3147 | ||
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| Author |
Oscar Amoros; Sergio Escalera; Anna Puig | ||||
| Title | Adaboost GPU-based Classifier for Direct Volume Rendering | Type | Conference Article | ||
| Year | 2011 | Publication | International Conference on Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 215-219 | ||
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| Abstract | In volume visualization, the voxel visibitity and materials are carried out through an interactive editing of Transfer Function. In this paper, we present a two-level GPU-based labeling method that computes in times of rendering a set of labeled structures using the Adaboost machine learning classifier. In a pre-processing step, Adaboost trains a binary classifier from a pre-labeled dataset and, in each sample, takes into account a set of features. This binary classifier is a weighted combination of weak classifiers, which can be expressed as simple decision functions estimated on a single feature values. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. We propose an alternative representation of these classifiers that allow a GPU-based parallelizated testing stage embedded into the visualization pipeline. The empirical results confirm the OpenCL-based classification of biomedical datasets as a tough problem where an opportunity for further research emerges. | ||||
| Address | Algarve, Portugal | ||||
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| Area | Expedition | Conference | GRAPP | ||
| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ AEP2011 | Serial | 1774 | ||
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| Author |
Oriol Vicente; Alicia Fornes; Ramon Valdes | ||||
| Title | The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities | Type | Conference Article | ||
| Year | 2016 | Publication | Digital Humanities Centres: Experiences and Perspectives | Abbreviated Journal | |
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| Address | Warsaw; Poland; December 2016 | ||||
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| Area | Expedition | Conference | DHLABS | ||
| Notes | DAG; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ VFV2016 | Serial | 2908 | ||
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| Author |
Oriol Vicente; Alicia Fornes; Ramon Valdes | ||||
| Title | La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades | Type | Conference Article | ||
| Year | 2017 | Publication | 3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional | Abbreviated Journal | |
| Volume | Issue | Pages | 281-383 | ||
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| ISSN | ISBN | 978-84-697-5692-8 | Medium | ||
| Area | Expedition | Conference | HDH | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ VFV2017 | Serial | 3060 | ||
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| Author |
Oriol Rodriguez-Leor; R. Hemetsberger; Francesco Ciompi; E Fernandez-Nofrerias; Angel Serrano; M. Bernet; Petia Radeva; F. Mauri; A. Bayes | ||||
| Title | Caracteritzacio automatica de la placa mitjançant analisis del espectre de radiofreqüencia en estudi de ecografia intracoronaria: resultat de la fusio de dades invivo i exvivo | Type | Conference Article | ||
| Year | 2010 | Publication | 22nd Congres Societat Catalana de Cardiologia, | Abbreviated Journal | |
| Volume | Issue | Pages | 131 | ||
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| Address | Barcelona (Spain) | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RHC2010 | Serial | 1368 | ||
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