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Author Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title Top-down model fitting for hand pose recovery in sequences of depth images Type Journal Article
  Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 79 Issue Pages 63-75  
  Keywords  
  Abstract State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.  
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  Notes HUPBA; 600.098 Approved no  
  Call Number Admin @ si @ MEC2018 Serial 3203  
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Author Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera edit   pdf
url  openurl
  Title Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification Type Journal Article
  Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 79 Issue Pages 76-85  
  Keywords  
  Abstract Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset.  
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  Notes HuPBA; 602.143 Approved no  
  Call Number Admin @ si @ JBE2018 Serial 3138  
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Author Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title Beyond Oneshot Encoding: lower dimensional target embedding Type Journal Article
  Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 75 Issue Pages 21-31  
  Keywords Error correcting output codes; Output embeddings; Deep learning; Computer vision  
  Abstract 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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  Notes ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 Approved no  
  Call Number Admin @ si @ RBE2018 Serial 3120  
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
openurl 
  Title Sign Language Recognition: A Deep Survey Type Journal Article
  Year 2021 Publication Expert Systems With Applications Abbreviated Journal ESWA  
  Volume 164 Issue Pages 113794  
  Keywords  
  Abstract Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ RKE2021a Serial 3521  
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad edit  url
openurl 
  Title Locality Regularized Group Sparse Coding for Action Recognition Type Journal Article
  Year 2017 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 158 Issue Pages 106-114  
  Keywords Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition  
  Abstract Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.  
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  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ BGE2017 Serial 3014  
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