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Author Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton edit  doi
openurl 
  Title Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis Type Journal Article
  Year 2016 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 28 Issue Pages 1489 - 1491  
  Keywords  
  Abstract The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others.  
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  Notes HuPBA; ISE;MV; Approved no  
  Call Number Admin @ si @ Serial 2851  
Permanent link to this record
 

 
Author Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans edit  url
openurl 
  Title Improved RGB-D-T based Face Recognition Type Journal Article
  Year 2016 Publication IET Biometrics Abbreviated Journal BIO  
  Volume 5 Issue 4 Pages 297 - 303  
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  Abstract Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.  
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  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ OCN2016 Serial 2854  
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Author Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund edit   pdf
url  openurl
  Title Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams Type Journal Article
  Year 2016 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 75 Issue 22 Pages 14985-14990  
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  Notes ISE; HUPBA Approved no  
  Call Number Admin @ si @ DDB2016 Serial 2934  
Permanent link to this record
 

 
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 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 Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats edit  doi
openurl 
  Title Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription Type Journal
  Year 2017 Publication Journal of Medical Engineering & Technology Abbreviated Journal JMET  
  Volume 41 Issue 6 Pages 486-497  
  Keywords  
  Abstract INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question.
OBJECTIVES:
To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination.
METHODS:
Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows.
RESULTS:
A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation).
CONCLUSIONS:
Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs.
 
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ EPB2017 Serial 3010  
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