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, Guillem Cucurull, Jordi Gonzalez, Josep M. Gonfaus, Kamal Nasrollahi, Thomas B. Moeslund, et al. (2017). Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification. Cyber - IEEE Transactions on cybernetics, , 1–11.
Abstract: Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database.
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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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Anjan Dutta, Josep Llados, Horst Bunke, & Umapada Pal. (2018). Product graph-based higher order contextual similarities for inexact subgraph matching. PR - Pattern Recognition, 76, 596–611.
Abstract: Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem.
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Karim Lekadir, Alfiia Galimzianova, Angels Betriu, Maria del Mar Vila, Laura Igual, Daniel L. Rubin, et al. (2017). A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. J-BHI - IEEE Journal Biomedical and Health Informatics, 21(1), 48–55.
Abstract: Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound.
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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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Anastasios Doulamis, Nikolaos Doulamis, Marco Bertini, Jordi Gonzalez, & Thomas B. Moeslund. (2016). Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams. MTAP - Multimedia Tools and Applications, 75(22), 14985–14990.
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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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Antonio Lopez, Atsushi Imiya, Tomas Pajdla, & Jose Manuel Alvarez. (2017). Computer Vision in Vehicle Technology: Land, Sea & Air. John Wiley & Sons, Ltd.
Abstract: Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition.
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Carles Sanchez, Debora Gil, T. Gache, N. Koufos, Marta Diez-Ferrer, & Antoni Rosell. (2016). SENSA: a System for Endoscopic Stenosis Assessment. In 28th Conference of the international Society for Medical Innovation and Technology.
Abstract: Documenting the severity of a static or dynamic Central Airway Obstruction (CAO) is crucial to establish proper diagnosis and treatment, predict possible treatment effects and better follow-up the patients. The subjective visual evaluation of a stenosis during video-bronchoscopy still remains the most common way to assess a CAO in spite of a consensus among experts for a need to standardize all calculations [1].
The Computer Vision Center in cooperation with the «Hospital de Bellvitge», has developed a System for Endoscopic Stenosis Assessment (SENSA), which computes CAO directly by analyzing standard bronchoscopic data without the need of using other imaging tecnologies.
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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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Lasse Martensson, Anders Hast, & Alicia Fornes. (2017). Word Spotting as a Tool for Scribal Attribution. In 2nd Conference of the association of Digital Humanities in the Nordic Countries (pp. 87–89).
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