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Iiris Lusi, Julio C. S. Jacques Junior, Jelena Gorbova, Xavier Baro, Sergio Escalera, Hasan Demirel, et al. (2017). Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
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.
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Chirster Loob, Pejman Rasti, Iiris Lusi, Julio C. S. Jacques Junior, Xavier Baro, Sergio Escalera, et al. (2017). Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
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.
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Pau Rodriguez, Jordi Gonzalez, Jordi Cucurull, Josep M. Gonfaus, & Xavier Roca. (2017). Regularizing CNNs with Locally Constrained Decorrelations. In 5th International Conference on Learning Representations.
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Hana Jarraya, Muhammad Muzzamil Luqman, & Jean-Yves Ramel. (2017). Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition. In B. Lamiroy, & R Dueire Lins (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 9657). LNCS. Springer.
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Umut Guclu, Yagmur Gucluturk, Meysam Madadi, Sergio Escalera, Xavier Baro, Jordi Gonzalez, et al. (2017). End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks.
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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H. Martin Kjer, Jens Fagertun, Sergio Vera, & Debora Gil. (2017). Medial structure generation for registration of anatomical structures. In Skeletonization, Theory, Methods and Applications (Vol. 11).
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Mireia Sole, Joan Blanco, Debora Gil, Oliver Valero, G. Fonseka, M. Lawrie, et al. (2017). Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study. In 11th European CytoGenesis Conference.
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Marc Bolaños, Alvaro Peris, Francisco Casacuberta, & Petia Radeva. (2017). VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering. In 8th Iberian Conference on Pattern Recognition and Image Analysis.
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.
Keywords: Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks
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Marta Diez-Ferrer, Debora Gil, Elena Carreño, Susana Padrones, Samantha Aso, Vanesa Vicens, et al. (2017). Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation. ERJ - European Respiratory Journal, .
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Quentin Angermann, Jorge Bernal, Cristina Sanchez Montes, Maroua Hammami, Gloria Fernandez Esparrach, Xavier Dray, et al. (2017). Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis. In 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery.
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Hana Jarraya, Oriol Ramos Terrades, & Josep Llados. (2017). Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs. In 8th Iberian Conference on Pattern Recognition and Image Analysis.
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.
Keywords: Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines
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Mireia Sole, Joan Blanco, Debora Gil, G. Fonseka, Richard Frodsham, Oliver Valero, et al. (2017). Unraveling the enigmas of chromosome territoriality during spermatogenesis. In IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia.
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Xinhang Song, Luis Herranz, & Shuqiang Jiang. (2017). Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs. In 31st AAAI Conference on Artificial Intelligence.
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.
Keywords: RGB-D scene recognition; weakly supervised; fine tune; CNN
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Simone Balocco, Francesco Ciompi, Juan Rigla, Xavier Carrillo, J. Mauri, & Petia Radeva. (2017). Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study. In International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT). LNCS.
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 identi cation 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.
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Aitor Alvarez-Gila, Joost Van de Weijer, & Estibaliz Garrote. (2017). Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB. In 1st International Workshop on Physics Based Vision meets Deep Learning.
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.
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