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Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
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Title |
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks |
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Year |
2021 |
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International Journal of Computer Vision |
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IJCV |
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129 |
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2499–2512 |
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In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. |
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HUPBA; no proj |
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Admin @ si @ MBE2021 |
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3654 |
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Author |
Francesco Ciompi; Oriol Pujol; Carlo Gatta; O. Rodriguez-Leor; J. Mauri; Petia Radeva |
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Title |
Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization |
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Journal Article |
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Year |
2010 |
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International Journal of Cardiovascular Imaging |
Abbreviated Journal |
IJCI |
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26 |
Issue |
7 |
Pages |
763–779 |
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Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect. |
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1569-5794 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ CPG2010 |
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1305 |
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Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya |
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Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” |
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2018 |
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Informaciones Psiquiatricas |
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232 |
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47-59 |
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0210-7279 |
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HUPBA; no menciona |
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Admin @ si @ FAA2018 |
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3214 |
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Author |
Oriol Pujol; Debora Gil; Petia Radeva |
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Title |
Fundamentals of Stop and Go active models |
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Journal Article |
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Year |
2005 |
Publication |
Image and Vision Computing |
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Volume |
23 |
Issue |
8 |
Pages |
681-691 |
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Deformable models; Geodesic snakes; Region-based segmentation |
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An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation. |
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Butterworth-Heinemann |
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Newton, MA, USA |
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0262-8856 |
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IAM;MILAB;HuPBA |
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IAM @ iam @ PGR2005 |
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1629 |
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Author |
Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez |
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Title |
Beyond Oneshot Encoding: lower dimensional target embedding |
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Journal Article |
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Year |
2018 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
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75 |
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21-31 |
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Keywords |
Error correcting output codes; Output embeddings; Deep learning; Computer vision |
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Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates. |
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ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 |
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Call Number |
Admin @ si @ RBE2018 |
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3120 |
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