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Author Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon edit  url
openurl 
  Title Looking at People Special Issue Type Journal Article
  Year 2018 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 126 Issue 2-4 Pages 141-143  
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  Notes HUPBA; ISE; 600.119 Approved no  
  Call Number Admin @ si @ EGJ2018 Serial 3093  
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Author Yunan Li; Jun Wan; Qiguang Miao; Sergio Escalera; Huijuan Fang; Huizhou Chen; Xiangda Qi; Guodong Guo edit  url
openurl 
  Title CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis Type Journal Article
  Year 2020 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 128 Issue Pages 2763–2780  
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  Abstract First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art.  
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  Notes HuPBA; no menciona Approved no  
  Call Number Admin @ si @ LWM2020 Serial 3413  
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Author Meysam Madadi; Hugo Bertiche; Sergio Escalera edit   pdf
doi  openurl
  Title Deep unsupervised 3D human body reconstruction from a sparse set of landmarks Type Journal Article
  Year 2021 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 129 Issue Pages 2499–2512  
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  Abstract 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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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ MBE2021 Serial 3654  
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Author Oriol Pujol; Petia Radeva edit  doi
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  Title Texture Segmentation by Statistical Deformable Models Type Journal
  Year 2004 Publication (up) International Journal of Image and Graphics Abbreviated Journal IJIG  
  Volume 4 Issue 3 Pages 433-452  
  Keywords Texture segmentation, parametric active contours, statistic snakes  
  Abstract Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PuR2004a Serial 505  
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Author Oriol Pujol; Petia Radeva edit  openurl
  Title Texture Segmentation by Statistic Deformable Models Type Journal
  Year 2003 Publication (up) International Journal of Image and Graphics (IJIG) Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PuR2003 Serial 432  
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