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Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera |
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Title |
Automatic Digital Biometry Analysis based on Depth Maps |
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Journal Article |
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Year |
2013 |
Publication |
Computers in Industry |
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COMPUTIND |
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64 |
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9 |
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1316-1325 |
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Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis |
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World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. |
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HuPBA;MILAB |
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Admin @ si @ RCR2013 |
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2252 |
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Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
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Title |
Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification |
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Journal Article |
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Year |
2015 |
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Pattern Analysis and Applications |
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PAA |
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18 |
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2 |
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247-261 |
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Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification |
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In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. |
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Springer-Verlag |
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1433-7541 |
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HuPBA;MILAB |
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Admin @ si @ PEP2013 |
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2251 |
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Albert Clapes; Miguel Reyes; Sergio Escalera |
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Title |
Multi-modal User Identification and Object Recognition Surveillance System |
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Journal Article |
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2013 |
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Pattern Recognition Letters |
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PRL |
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34 |
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7 |
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799-808 |
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Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning |
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We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. |
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Elsevier |
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HUPBA; 600.046; 605.203;MILAB |
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Admin @ si @ CRE2013 |
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2248 |
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Author |
Mohammad ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
10 |
Pages |
2830-2839 |
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Keywords |
Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification |
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Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. |
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Elsevier |
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0031-3203 |
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HuPBA;MILAB |
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no |
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Admin @ si @ BGE2013a |
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2247 |
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Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; Josefina Mauri; Petia Radeva |
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Title |
Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation |
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Journal Article |
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Year |
2013 |
Publication |
Ultrasound in Medicine and Biology |
Abbreviated Journal |
UMB |
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Volume |
39 |
Issue |
9 |
Pages |
1698-712 |
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Keywords |
Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy |
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Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages. |
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MILAB |
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Admin @ si @ ABC2013 |
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2313 |
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