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Author Iiris Lusi; Julio C. S. Jacques Junior; Jelena Gorbova; Xavier Baro; Sergio Escalera; Hasan Demirel; Juri Allik; Cagri Ozcinar; Gholamreza Anbarjafari
Title Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases Type Conference Article
Year 2017 Publication 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
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Abstract In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.
Address Washington; DC; USA; May 2017
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Area Expedition Conference FG
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ LJG2017 Serial 2924
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Author Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari
Title Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification Type Conference Article
Year 2017 Publication 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
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Abstract We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions.
Address Washington; DC; USA; May 2017
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Area Expedition Conference FG
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ LRL2017 Serial 2925
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Author Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca
Title Regularizing CNNs with Locally Constrained Decorrelations Type Conference Article
Year 2017 Publication 5th International Conference on Learning Representations Abbreviated Journal
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Address Toulon; France; April 2017
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Publisher Place of Publication Editor
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Area Expedition Conference ICLR
Notes ISE; 602.143; 600.119; 600.098 Approved no
Call Number Admin @ si @ RGC2017 Serial 2927
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Author Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel
Title Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition Type Book Chapter
Year 2017 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages (up)
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Publisher Springer Place of Publication Editor B. Lamiroy; R Dueire Lins
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JLR2017 Serial 2928
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Author Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven
Title End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks Type Miscellaneous
Year 2017 Publication Arxiv Abbreviated Journal
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Abstract arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them.
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Notes HuPBA; ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ GGM2017 Serial 2932
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Author H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil
Title Medial structure generation for registration of anatomical structures Type Book Chapter
Year 2017 Publication Skeletonization, Theory, Methods and Applications Abbreviated Journal
Volume 11 Issue Pages (up)
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Notes IAM; 600.096; 600.075; 600.145 Approved no
Call Number Admin @ si @ MFV2017a Serial 2935
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Author Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate
Title Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study Type Conference Article
Year 2017 Publication 11th European CytoGenesis Conference Abbreviated Journal
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Address Florencia; Italia; July 2017
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Publisher Place of Publication Editor
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Area Expedition Conference ECA
Notes IAM; 600.096; 600.145 Approved no
Call Number Admin @ si @ SBG2017a Serial 2936
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Author Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva
Title VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering Type Conference Article
Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
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Keywords Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks
Abstract In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.
Address Faro; Portugal; June 2017
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Area Expedition Conference IbPRIA
Notes MILAB; no proj Approved no
Call Number Admin @ si @ BPC2017 Serial 2939
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Author Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell
Title Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation Type Journal Article
Year 2017 Publication European Respiratory Journal Abbreviated Journal ERJ
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Notes IAM Approved no
Call Number Admin @ si @ DGC2017b Serial 3632
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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
Title Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis Type Conference Article
Year 2017 Publication 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal
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Address Barcelona; Spain; June 2017
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Area Expedition Conference CARS
Notes MV; no menciona Approved no
Call Number Admin @ si @ ABS2017 Serial 2947
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
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Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines
Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.
Address Faro; Portugal; June 2017
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Area Expedition Conference IbPRIA
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017a Serial 2953
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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
Title Unraveling the enigmas of chromosome territoriality during spermatogenesis Type Conference Article
Year 2017 Publication IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia Abbreviated Journal
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Address UAB; Barcelona; June 2017
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Notes IAM; 600.145 Approved no
Call Number Admin @ si @ SBG2017b Serial 2959
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Author Xinhang Song; Luis Herranz; Shuqiang Jiang
Title Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs Type Conference Article
Year 2017 Publication 31st AAAI Conference on Artificial Intelligence Abbreviated Journal
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Keywords RGB-D scene recognition; weakly supervised; fine tune; CNN
Abstract Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.
Address San Francisco CA; February 2017
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Area Expedition Conference AAAI
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ SHJ2017 Serial 2967
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Author Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva
Title Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study Type Conference Article
Year 2017 Publication International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) Abbreviated Journal
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Abstract An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback.
The pipeline of the method requires the identication of the stable frames
of the sequence and the reliable detection of stent struts. Using this data,
a measure of likelihood for a frame to contain a stent is computed. Then,
a robust binary representation of the presence of the stent in the pullback
is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores.
Address Quebec; Canada; September 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
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Area Expedition Conference MICCAIW
Notes MILAB; no proj Approved no
Call Number Admin @ si @ BCR2017 Serial 2968
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Author Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote
Title Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB Type Conference Article
Year 2017 Publication 1st International Workshop on Physics Based Vision meets Deep Learning Abbreviated Journal
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Abstract Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer.
Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However,
most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset.
Address Venice; Italy; October 2017
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Area Expedition Conference ICCV-PBDL
Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ AWG2017 Serial 2969
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