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Author Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari edit  doi
openurl 
  Title Dominant and Complementary Emotion Recognition from Still Images of Faces Type Journal Article
  Year (down) 2018 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume 6 Issue Pages 26391 - 26403  
  Keywords  
  Abstract Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ GLW2018 Serial 3122  
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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 (down) 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 Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler edit  url
openurl 
  Title Articulated Motion and Deformable Objects Type Journal Article
  Year (down) 2018 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 79 Issue Pages 55-64  
  Keywords  
  Abstract This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ WEP2018 Serial 3126  
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Author Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund edit  doi
openurl 
  Title Organ Segmentation in Poultry Viscera Using RGB-D Type Journal Article
  Year (down) 2018 Publication Sensors Abbreviated Journal SENS  
  Volume 18 Issue 1 Pages 117  
  Keywords semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN  
  Abstract We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ PVJ2018 Serial 3072  
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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 (down) 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 79 Issue Pages 63-75  
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  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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