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Enhanced Human Parsing with Multiple Feature Fusion and Augmented Pose Model
Zhaoxiang Zhang; Jianliang Hao; Yunhong Wang; Yuhang Zhao
2014-08-24
Conference NameInternational Conference on Pattern Recognition
Source PublicationICPR 2014
Conference Date24-28 August 2014
Conference PlaceStockholm, Sweden
AbstractWe address the problem of human pose estimation, which is a very challenging problem due to view angle variance, noise and occlusions. In this paper, we propose a novel human parsing method which can estimate diverse human poses from real world images. We merge the parallel lines feature and uniform LBP feature, thereby the new feature contains both shape and texture information, which can be used by discriminative body part detectors. The standard tree model is augmented by using virtual nodes in order to describe the correlations between originally unconnected nodes, which enhances the robustness of the traditional kinematic tree model. We test our method in a sports image dataset, and the experimental results demonstrate the advantages of the merged feature as well as the augmented pose model in real applications.
KeywordEstimation Kinematics Image Edge Detection Heuristic Algorithms Feature Extraction Biological System Modeling Inference Algorithms
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13239
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Zhaoxiang Zhang,Jianliang Hao,Yunhong Wang,et al. Enhanced Human Parsing with Multiple Feature Fusion and Augmented Pose Model[C],2014.
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