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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 (down) 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.  
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  Publisher Elsevier B.V. Place of Publication Editor  
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  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ CEA2016 Serial 2768  
Permanent link to this record
 

 
Author Jean-Pascal Jacob; Mariella Dimiccoli; Lionel Moisan edit   pdf
doi  openurl
  Title Active skeleton for bacteria modeling Type Journal Article
  Year (down) 2016 Publication Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization Abbreviated Journal CMBBE  
  Volume 5 Issue 4 Pages 274-286  
  Keywords Bacteria modelling; medial axis; active contours; active skeleton; shape contraints  
  Abstract The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimizing an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at this http URL  
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  Notes MILAB Approved no  
  Call Number Admin @ si @ JDM2016 Serial 2711  
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Author M. Oliver; G. Haro; Mariella Dimiccoli; B. Mazin; C. Ballester edit   pdf
doi  openurl
  Title A Computational Model for Amodal Completion Type Journal Article
  Year (down) 2016 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 56 Issue 3 Pages 511–534  
  Keywords Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica  
  Abstract This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth.
Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.
 
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  Area Expedition Conference  
  Notes MILAB; 601.235 Approved no  
  Call Number Admin @ si @ OHD2016b Serial 2745  
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva edit   pdf
doi  openurl
  Title Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams Type Journal Article
  Year (down) 2016 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 149 Issue Pages 146-156  
  Keywords  
  Abstract Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.  
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  Notes MILAB; Approved no  
  Call Number Admin @ si @ ADR2016b Serial 2742  
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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 (down) 2016 Publication IET Biometrics Abbreviated Journal BIO  
  Volume 5 Issue 4 Pages 297 - 303  
  Keywords  
  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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  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ OCN2016 Serial 2854  
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