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Author Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo edit  url
openurl 
  Title Subspace Procrustes Analysis Type Journal Article
  Year 2017 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 121 Issue 3 Pages 327–343  
  Keywords  
  Abstract Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach.
 
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  Notes MILAB; HuPBA; no proj Approved no  
  Call Number Admin @ si @ PTI2017 Serial 2841  
Permanent link to this record
 

 
Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
openurl 
  Title Hand pose aware multimodal isolated sign language recognition Type Journal Article
  Year 2020 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 80 Issue Pages 127–163  
  Keywords  
  Abstract Isolated hand sign language recognition from video is a challenging research area in computer vision. Some of the most important challenges in this area include dealing with hand occlusion, fast hand movement, illumination changes, or background complexity. While most of the state-of-the-art results in the field have been achieved using deep learning-based models, the previous challenges are not completely solved. In this paper, we propose a hand pose aware model for isolated hand sign language recognition using deep learning approaches from two input modalities, RGB and depth videos. Four spatial feature types: pixel-level, flow, deep hand, and hand pose features, fused from both visual modalities, are input to LSTM for temporal sign recognition. While we use Optical Flow (OF) for flow information in RGB video inputs, Scene Flow (SF) is used for depth video inputs. By including hand pose features, we show a consistent performance improvement of the sign language recognition model. To the best of our knowledge, this is the first time that this discriminant spatiotemporal features, benefiting from the hand pose estimation features and multi-modal inputs, are fused for isolated hand sign language recognition. We perform a step-by-step analysis of the impact in terms of recognition performance of the hand pose features, different combinations of the spatial features, and different recurrent models, especially LSTM and GRU. Results on four public datasets confirm that the proposed model outperforms the current state-of-the-art models on Montalbano II, MSR Daily Activity 3D, and CAD-60 datasets with a relative accuracy improvement of 1.64%, 6.5%, and 7.6%. Furthermore, our model obtains a competitive results on isoGD dataset with only 0.22% margin lower than the current state-of-the-art model.  
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ RKE2020 Serial 3524  
Permanent link to this record
 

 
Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
doi  openurl
  Title Video-based Isolated Hand Sign Language Recognition Using a Deep Cascaded Model Type Journal Article
  Year 2020 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 79 Issue Pages 22965–22987  
  Keywords  
  Abstract In this paper, we propose an efficient cascaded model for sign language recognition taking benefit from spatio-temporal hand-based information using deep learning approaches, especially Single Shot Detector (SSD), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM), from videos. Our simple yet efficient and accurate model includes two main parts: hand detection and sign recognition. Three types of spatial features, including hand features, Extra Spatial Hand Relation (ESHR) features, and Hand Pose (HP) features, have been fused in the model to feed to LSTM for temporal features extraction. We train SSD model for hand detection using some videos collected from five online sign dictionaries. Our model is evaluated on our proposed dataset (Rastgoo et al., Expert Syst Appl 150: 113336, 2020), including 10’000 sign videos for 100 Persian sign using 10 contributors in 10 different backgrounds, and isoGD dataset. Using the 5-fold cross-validation method, our model outperforms state-of-the-art alternatives in sign language recognition  
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  Notes HuPBA; no menciona Approved no  
  Call Number Admin @ si @ RKE2020b Serial 3442  
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Author Egils Avots; Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Baro; Paul Pallin; Gholamreza Anbarjafari edit   pdf
url  doi
openurl 
  Title From 2D to 3D geodesic-based garment matching Type Journal Article
  Year 2019 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 78 Issue 18 Pages 25829–25853  
  Keywords Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model  
  Abstract A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset.  
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  Notes HuPBA; ISE; 600.098; 600.119; 602.133 Approved no  
  Call Number Admin @ si @ AME2019 Serial 3317  
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Author Andre Litvin; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Thomas B. Moeslund; Gholamreza Anbarjafari edit  url
openurl 
  Title A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition Type Journal Article
  Year 2019 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 78 Issue 18 Pages 25259–25271  
  Keywords Fully convolutional networks; FusionNet; Thermal imaging; Face recognition  
  Abstract This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network.  
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  Notes HuPBA; no menciona Approved no  
  Call Number Admin @ si @ LNE2019 Serial 3318  
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