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Author Gerard Canal; Sergio Escalera; Cecilio Angulo edit   pdf
doi  openurl
  Title A Real-time Human-Robot Interaction system based on gestures for assistive scenarios Type Journal Article
  Year 2016 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 149 Issue Pages 65-77  
  Keywords Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation  
  Abstract Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times.  
  Address  
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  Publisher Elsevier B.V. Place of Publication Editor  
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  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ CEA2016 Serial 2768  
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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  
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Author Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio edit  doi
openurl 
  Title Computing quantitative indicators of structural renal damage in pediatric DMSA scans Type Journal Article
  Year 2017 Publication Revista Española de Medicina Nuclear e Imagen Molecular Abbreviated Journal REMNIM  
  Volume 36 Issue 2 Pages 72-77  
  Keywords  
  Abstract OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner.
MATERIALS AND METHODS:
From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan.
RESULTS:
A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%).
CONCLUSIONS:
The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators.
 
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  Notes HuPBA;MILAB; no menciona Approved no  
  Call Number Admin @ si @ SDE2017 Serial 2842  
Permanent link to this record
 

 
Author Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund edit  url
openurl 
  Title Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields Type Journal Article
  Year 2016 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 80 Issue Pages 208–215  
  Keywords  
  Abstract This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.
The approach shows that simple multi-modal features with the power of the MMSSL
paradigm can achieve better performance than state of the art results on the same dataset.
 
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  Notes HuPBA; ISE;MILAB; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ TEG2016 Serial 2843  
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Author Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla edit  doi
openurl 
  Title Editorial: Special Issue on Computational Intelligence for Vision and Robotics Type Journal Article
  Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications  
  Volume 28 Issue 5 Pages 853–854  
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  Notes HuPBA;MILAB; no menciona Approved no  
  Call Number Admin @ si @ GGE2017 Serial 2845  
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