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Author |
Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera |
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Title |
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions |
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Conference Article |
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Year |
2019 |
Publication |
Visual Recognition for Medical Images workshop |
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Pages |
406-415 |
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Abstract |
In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. |
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Seul; Korea; October 2019 |
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ICCVW |
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HUPBA; no proj |
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no |
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Call Number  |
Admin @ si @ AAF2019 |
Serial |
3324 |
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Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera |
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Title |
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation |
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Conference Article |
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Year |
2020 |
Publication |
Bioimage computation workshop |
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Virtual; August 2020 |
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ECCVW |
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HUPBA |
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no |
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Admin @ si @ AAF2020 |
Serial |
3520 |
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Author |
Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera |
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Title |
Deep Frequency Re-Calibration U-Net for Medical Image Segmentation |
Type |
Conference Article |
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Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
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3274-3283 |
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We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
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VIRTUAL; October 2021 |
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ICCVW |
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HUPBA; no proj |
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no |
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Admin @ si @ ABA2021 |
Serial |
3645 |
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Author |
Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto |
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Title |
Va-ID: A trust-based virtual assessment system |
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Conference Article |
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Year |
2014 |
Publication |
6th International Conference on Intelligent Networking and Collaborative Systems |
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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. |
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Address |
Salerna; Italy; September 2014 |
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978-1-4799-6386-7 |
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INCOS |
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Notes |
OR; HuPBA;MV |
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no |
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Call Number |
Admin @ si @ ABB2014 |
Serial |
2620 |
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Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
Automatic Non-Rigid Temporal Alignment of IVUS Sequences |
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Conference Article |
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Year |
2012 |
Publication |
15th International Conference on Medical Image Computing and Computer Assisted Intervention |
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Volume |
1 |
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Pages |
642-650 |
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Abstract |
Clinical studies on atherosclerosis regression/progression performed by Intravascular Ultrasound analysis require the alignment of pullbacks of the same patient before and after clinical interventions. In this paper, a methodology for the automatic alignment of IVUS sequences based on the Dynamic Time Warping technique is proposed. The method is adapted to the specific IVUS alignment task by applying the non-rigid alignment technique to multidimensional morphological signals, and by introducing a sliding window approach together with a regularization term. To show the effectiveness of our method, an extensive validation is performed both on synthetic data and in-vivo IVUS sequences. The proposed method is robust to stent deployment and post dilation surgery and reaches an alignment error of approximately 0.7 mm for in-vivo data, which is comparable to the inter-observer variability. |
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Address |
Nice, France |
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Springer-Verlag Berlin, Heidelberg |
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978-3-642-33414-6 |
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MICCAI |
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MILAB |
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no |
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Call Number |
Admin @ si @ ABC2012 |
Serial |
2168 |
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Author |
Fernando Alonso; Xavier Baro; Sergio Escalera; Jordi Gonzalez; Martha Mackay; Anna Serrahima |
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Title |
CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions |
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Conference Article |
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2016 |
Publication |
16th International Conference for Integrated Care |
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Poster |
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Address |
Barcelona; Spain; May 2016 |
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ICIC |
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Notes |
HuPBA; ISE;MV |
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no |
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Call Number |
Admin @ si @ ABE2016 |
Serial |
2855 |
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Author |
Ana Maria Ares; Jorge Bernal; Maria Jesus Nozal; F. Javier Sanchez; Jose Bernal |
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Title |
Results of the use of Kahoot! gamification tool in a course of Chemistry |
Type |
Conference Article |
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Year |
2018 |
Publication |
4th International Conference on Higher Education Advances |
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Pages |
1215-1222 |
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Abstract |
The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance. |
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Valencia; June 2018 |
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HEAD |
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Notes |
MV; no proj |
Approved |
no |
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Call Number |
Admin @ si @ ABN2018 |
Serial |
3246 |
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Author |
Maryam Asadi-Aghbolaghi; Hugo Bertiche; Vicent Roig; Shohreh Kasaei; Sergio Escalera |
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Title |
Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies |
Type |
Conference Article |
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Year |
2017 |
Publication |
Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV |
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Venice; Italy; October 2017 |
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ICCVW |
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HUPBA; no menciona |
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no |
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Admin @ si @ ABR2017 |
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3068 |
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Author |
Cristhian Aguilera; Fernando Barrera; Angel Sappa; Ricardo Toledo |
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Title |
A Novel SIFT-Like-Based Approach for FIR-VS Images Registration |
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Conference Article |
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Year |
2012 |
Publication |
11th Quantitative InfraRed Thermography |
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Naples, Italy |
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QIRT |
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ADAS; TV |
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no |
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Admin @ si @ ABS2012 |
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2017 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis |
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Conference Article |
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2017 |
Publication |
31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
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Barcelona; Spain; June 2017 |
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CARS |
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MV; no menciona |
Approved |
no |
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Admin @ si @ ABS2017 |
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2947 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Gloria Fernandez Esparrach; Xavier Gray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis |
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Conference Article |
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2017 |
Publication |
4th International Workshop on Computer Assisted and Robotic Endoscopy |
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29-41 |
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Polyp detection; colonoscopy; real time; spatio temporal coherence |
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Abstract |
Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all
polyps under real time constraints, increasing its performance due to our
adaptation strategy. |
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Quebec; Canada; September 2017 |
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CARE |
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MV; 600.096; 600.075 |
Approved |
no |
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Call Number |
Admin @ si @ ABS2017b |
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2977 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Clinical Usability Quantification Of a Real-Time Polyp Detection Method In Videocolonoscopy |
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2017 |
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25th United European Gastroenterology Week |
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Barcelona, October 2017 |
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ESGE |
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MV; no menciona |
Approved |
no |
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Admin @ si @ ABS2017c |
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2978 |
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Author |
Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera |
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Title |
A survey on deep learning based approaches for action and gesture recognition in image sequences |
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Conference Article |
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2017 |
Publication |
12th IEEE International Conference on Automatic Face and Gesture Recognition |
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The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning
for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions.
We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research. |
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Washington; USA; May 2017 |
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FG |
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HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ ACB2017b |
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2982 |
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Author |
Ruth Aylett; Ginevra Castellano; Bogdan Raducanu; Ana Paiva; Marc Hanheide |
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Title |
Long-term socially perceptive and interactive robot companions: challenges and future perspectives |
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Conference Article |
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Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
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323-326 |
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human-robot interaction, multimodal interaction, social robotics |
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This paper gives a brief overview of the challenges for multi-model perception and generation applied to robot companions located in human social environments. It reviews the current position in both perception and generation and the immediate technical challenges and goes on to consider the extra issues raised by embodiment and social context. Finally, it briefly discusses the impact of systems that must function continually over months rather than just for a few hours. |
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Address |
Alicante |
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ACM |
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978-1-4503-0641-6 |
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ICMI |
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OR;MV |
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Admin @ si @ ACR2011 |
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1888 |
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Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Bemedi |
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Title |
Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars |
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Conference Article |
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2013 |
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6th Iberian Conference on Pattern Recognition and Image Analysis |
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7887 |
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133-140 |
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In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model. |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38627-5 |
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IbPRIA |
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DAG; 605.203 |
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Admin @ si @ ACS2013 |
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2328 |
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