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Author |
Sergio Escalera |
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Title |
Multi-Modal Human Behaviour Analysis from Visual Data Sources |
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Journal |
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Year |
2013 |
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ERCIM News journal |
Abbreviated Journal |
ERCIM |
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Volume |
95 |
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21-22 |
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The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction. |
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0926-4981 |
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HuPBA;MILAB |
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Admin @ si @ Esc2013 |
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2361 |
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Author |
Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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Title |
Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
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18 |
Issue |
6 |
Pages |
1831-1838 |
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Keywords |
Wireless Capsule Endoscopy; Small Bowel Motility Dysfunction; Contraction Detection; Structured Prediction; Betweenness Centrality |
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Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the folding of the intestinal wall. In this paper we present a new method to robustly detect wrinkle frames in full WCE videos by using a new mid-level image descriptor that is based on a centrality measure proposed for graphs. We present an extended validation, carried out in a very large database, that shows that the proposed method achieves state of the art performance for this task. |
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OR; MILAB; 600.046;MV |
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Admin @ si @ SDZ2014 |
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2385 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Combining Local and Global Learners in the Pairwise Multiclass Classification |
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Year |
2015 |
Publication |
Pattern Analysis and Applications |
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PAA |
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18 |
Issue |
4 |
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845-860 |
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Multiclass classification; Pairwise approach; One-versus-one |
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Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. |
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Springer London |
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1433-7541 |
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HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ BGE2014 |
Serial |
2441 |
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Author |
Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez |
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Title |
Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) |
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TSMCB |
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44 |
Issue |
12 |
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2379-2390 |
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The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. |
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2168-2267 |
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Notes |
HuPBA; ISE; 600.078;MILAB |
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no |
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Call Number |
Admin @ si @ LRE2014 |
Serial |
2442 |
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Author |
Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Jordi Gonzalez |
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Title |
A survey on model based approaches for 2D and 3D visual human pose recovery |
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Journal Article |
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Year |
2014 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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Volume |
14 |
Issue |
3 |
Pages |
4189-4210 |
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Keywords |
human pose recovery; human body modelling; behavior analysis; computer vision |
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Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature. |
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HuPBA; ISE; 600.046; 600.063; 600.078;MILAB |
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Admin @ si @ PEA2014 |
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2443 |
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