The Experts below are selected from a list of 53808 Experts worldwide ranked by ideXlab platform
Zhanpeng Shao - One of the best experts on this subject based on the ideXlab platform.
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A Hierarchical Model for Human Action Recognition From Body-Parts
IEEE Transactions on Circuits and Systems for Video Technology, 2019Co-Authors: Zhanpeng Shao, Youfu Li, Xiaolong Zhou, Shengyong ChenAbstract:As increasing attention is paid to human action recognition from skeleton data, this paper focuses on such tasks by proposing a Hierarchical Model to discover the structure information of body-parts involved in actions for better analysis of human actions in the skeleton data. Considering human actions as simultaneous motions of body-parts of the human skeleton, we propose a Hierarchical Model to simultaneously apply discriminative body-parts selection at a same scale and group coupling of bundles of body-parts at different scales, while we decompose the human skeleton into a hierarchy of body-parts of varying scales. To represent such hierarchy of body-parts, we accordingly build a Hierarchical rotation and relative velocity (HRRV) descriptor. The Hierarchical representations encoded by Fisher vectors of the HRRV descriptors are properly formulated into the Hierarchical Model via the proposed mixed norm, to apply the sparse selection of body-parts and regularize the structure of such hierarchy of body-parts. The extensive evaluations on three challenging datasets demonstrate the effectiveness of our proposed approach, which achieves superior performance compared to the state-of-the-art algorithms on datasets with various sizes, showing it is more widely applicable than existing approaches.
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A Hierarchical Model for Action Recognition Based on Body Parts
2018 IEEE International Conference on Robotics and Automation (ICRA), 2018Co-Authors: Zhanpeng Shao, Youfu Li, Jianyu Yang, Zhenhua WangAbstract:As increasing attention is paid on human action recognition from skeleton data, this paper focuses on such tasks by proposing a Hierarchical Model to discover the structure information of body-parts involved in human actions. Considering human actions as simultaneous motions of different body-parts of the human skeleton, we propose a Hierarchical Model to simultaneously apply discriminative body-parts selection at a same scale and group coupling of bundles of body-parts at different scales, while we decompose the human skeleton into a hierarchy of body-parts of varying scales. To represent such hierarchy of body-parts, we accordingly build a Hierarchical RRV (Rotation and Relative Velocity) descriptors. The Hierarchical representations encoded by Fisher vectors of the Hierarchical RRV descriptors are properly formulated into the Hierarchical Model via the proposed Hierarchical mixed norm, to apply sparse selection of body-parts and regularize the structure of such hierarchy of body-parts. The extensive evaluations on three challenging datasets demonstrate the effectiveness of our proposed approach, which achieves superior performance compared to state-of-the-art results on different sizes of datasets, showing it is more widely applicable than existing approaches.
C. He - One of the best experts on this subject based on the ideXlab platform.
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Supervised SAR Image MPM Segmentation Based on Region-Based Hierarchical Model
IEEE Geoscience and Remote Sensing Letters, 2006Co-Authors: Y. Yang, C. HeAbstract:This letter presents a novel method of supervised multiresolution segmentation for synthetic aperture radar images. The method uses a region-based half-tree Hierarchical Markov random field Model for multiresolution segmentation. To form the region-based multilayer Model, the watershed algorithm is employed at each resolution level independently. The nodes of a quadtree in the proposed Model are defined as regions instead of pixels. The relationship over scale is studied, and the region-based upward and downward maximization of posterior marginal estimations are deduced. The experimental results for the segmentation of homogeneous areas prove the region-based Model much better in terms of robustness to speckle and preservation of edges compared to the pixel-based Hierarchical Model and the Gibbs sampler with the single-resolution Model
Peter Wittwer - One of the best experts on this subject based on the ideXlab platform.
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a nontrivial renormalization group fixed point for the dyson baker Hierarchical Model
Communications in Mathematical Physics, 1994Co-Authors: Hans Koch, Peter WittwerAbstract:We prove the existence of a nontrivial Renormalization Group (RG) fixed point for the Dyson-Baker Hierarchical Model ind=3 dimensions. The single spin distribution of the fixed point is shown to be entire analytic, and bounded by exp(−const×t6) for large real values of the spint. Our proof is based on estimates for the zeros of a RG fixed point for Gallavotti's Hierarchical Model. We also present some general results for the heat flow on a space of entire functions, including an order preserving property for zeros, which is used in the RG analysis.
Zhenhua Wang - One of the best experts on this subject based on the ideXlab platform.
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A Hierarchical Model for Action Recognition Based on Body Parts
2018 IEEE International Conference on Robotics and Automation (ICRA), 2018Co-Authors: Zhanpeng Shao, Youfu Li, Jianyu Yang, Zhenhua WangAbstract:As increasing attention is paid on human action recognition from skeleton data, this paper focuses on such tasks by proposing a Hierarchical Model to discover the structure information of body-parts involved in human actions. Considering human actions as simultaneous motions of different body-parts of the human skeleton, we propose a Hierarchical Model to simultaneously apply discriminative body-parts selection at a same scale and group coupling of bundles of body-parts at different scales, while we decompose the human skeleton into a hierarchy of body-parts of varying scales. To represent such hierarchy of body-parts, we accordingly build a Hierarchical RRV (Rotation and Relative Velocity) descriptors. The Hierarchical representations encoded by Fisher vectors of the Hierarchical RRV descriptors are properly formulated into the Hierarchical Model via the proposed Hierarchical mixed norm, to apply sparse selection of body-parts and regularize the structure of such hierarchy of body-parts. The extensive evaluations on three challenging datasets demonstrate the effectiveness of our proposed approach, which achieves superior performance compared to state-of-the-art results on different sizes of datasets, showing it is more widely applicable than existing approaches.
Tom J Brown - One of the best experts on this subject based on the ideXlab platform.
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on the trait antecedents and outcomes of service worker job resourcefulness a Hierarchical Model approach
Journal of the Academy of Marketing Science, 2003Co-Authors: Jane W Licata, John C Mowen, Eric G Harris, Tom J BrownAbstract:In a series of three studies, a four-level Hierarchical Model of personality was employed to identify the antecedents and three validating criteria of a newly developed trait labeledjob resourcefulness (JR). JR is defined as an enduring disposition to garner scarce resources and overcome obstacles in pursuit of job-related goals. Across three service contexts, JR was shown to predict customer orientation, self-rated performance, and supervisor-rated performance. The results also revealed that the Hierarchical Model accounted for more variance in performance ratings than one version of the 5-Factor Model of personality. Results are discussed in terms of their implications for selecting high-performing service employees.
