Enhanced Human Parsing with Multiple Feature Fusion and Augmented Pose Model
文献类型：会议论文
| 作者 | Zhaoxiang Zhang ; Jianliang Hao; Yunhong Wang; Yuhang Zhao
|
| 出版日期 | 2014-08-24 |
| 会议日期 | 24-28 August 2014 |
| 会议地点 | Stockholm, Sweden |
| 关键词 | Estimation Kinematics Image Edge Detection Heuristic Algorithms Feature Extraction Biological System Modeling Inference Algorithms |
| 英文摘要 | We 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. |
| 会议录 | ICPR 2014
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13239]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhaoxiang Zhang |
| 推荐引用方式 GB/T 7714 | Zhaoxiang Zhang,Jianliang Hao,Yunhong Wang,et al. Enhanced Human Parsing with Multiple Feature Fusion and Augmented Pose Model[C]. 见:. Stockholm, Sweden. 24-28 August 2014. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。