Grasp type understanding - classification, localization and clustering

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Grasp type understanding - classification, localization and clustering
	Li, Yinlin; Zhang, Yuren; Qiao, Hong; Chen, Ken; Xi, Xuanyang; Li, YL
	2016
会议名称	12th World Congress on Intelligent Control and Automation (WCICA)
会议录名称	PROCEEDINGS OF THE 2016 12TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA)
会议日期	JUN 12-15, 2016
会议地点	Guilin, PEOPLES R CHINA
摘要	Prehensile analysis is a research field attracting multi-disciplinary interests, including computer science, mechanology and neuroscience. For robot, grasp type recognition provides critical information for human-robot interaction and robot self-learning. One of the research direction is to discover the common modes of human hand use with first-person point-of-view wearable cameras. In contrast to previous methods based on handcraft features and multi-stage pipeline, we use a convolutional neural network to learn discriminative features of grasp types automatically, which can also achieve grasptype localization and classification simultaneously in a single-stage pipeline. Furthermore, a clusteringmethod is also proposed to find the hierarchical relationships between different grasp types. Experiments are conducted on UT Grasp dataset and Yale human grasping dataset. The proposed method shows better accuracy and higher efficiency than traditional methods.
关键词	Objects Hands
学科领域	Automation & Control Systems ; Engineering, Electrical & Electronic
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12825
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Li, YL
推荐引用方式 GB/T 7714	Li, Yinlin,Zhang, Yuren,Qiao, Hong,et al. Grasp type understanding - classification, localization and clustering[C],2016.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
grasp.pdf（576KB）	会议论文		开放获取	CC BY-NC-SA	浏览