Workpiece localization with shadow detection and removing

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

	Workpiece localization with shadow detection and removing; Workpiece localization with shadow detection and removing; Workpiece localization with shadow detection and removing; Workpiece localization with shadow detection and removing; Workpiece localization with shadow detection and removing
	Qin, Zhengke; Zhu, Wenjun; Wang, Peng; Qiao, Hong
	2013-12 ; 2013-12 ; 2013-12 ; 2013-12 ; 2013-12
会议名称	Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on ; Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on ; Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on ; Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on ; Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on
会议日期	12-14 Dec. 2013 ; 12-14 Dec. 2013 ; 12-14 Dec. 2013 ; 12-14 Dec. 2013 ; 12-14 Dec. 2013
会议地点	Shenzhen, China ; Shenzhen, China ; Shenzhen, China ; Shenzhen, China ; Shenzhen, China
摘要	This paper presents a new approach to detect and remove the shadows for workpiece localization, which is with an extensive application in automatic assembly system. However, the shadows of workpiece will badly affect this procedure as the contour of the shadow has the same shape with the workpiece itself in the image. The localization system treats the shadow as a part of the workpiece and make incorrect decision. So removing the shadow in the image before localization is meaningful. Our approach use CAD model to estimate the pose of workpiece, and the contour of object can be drawn in the image. Gray and texture features are used to detect and remove the shadow around the workpiece, and the workpiece is localized without the disturbance of the shadow in image. Experiments have been designed and performed. The experimental results demonstrate the effectiveness of the proposed method. ; This paper presents a new approach to detect and remove the shadows for workpiece localization, which is with an extensive application in automatic assembly system. However, the shadows of workpiece will badly affect this procedure as the contour of the shadow has the same shape with the workpiece itself in the image. The localization system treats the shadow as a part of the workpiece and make incorrect decision. So removing the shadow in the image before localization is meaningful. Our approach use CAD model to estimate the pose of workpiece, and the contour of object can be drawn in the image. Gray and texture features are used to detect and remove the shadow around the workpiece, and the workpiece is localized without the disturbance of the shadow in image. Experiments have been designed and performed. The experimental results demonstrate the effectiveness of the proposed method. ; This paper presents a new approach to detect and remove the shadows for workpiece localization, which is with an extensive application in automatic assembly system. However, the shadows of workpiece will badly affect this procedure as the contour of the shadow has the same shape with the workpiece itself in the image. The localization system treats the shadow as a part of the workpiece and make incorrect decision. So removing the shadow in the image before localization is meaningful. Our approach use CAD model to estimate the pose of workpiece, and the contour of object can be drawn in the image. Gray and texture features are used to detect and remove the shadow around the workpiece, and the workpiece is localized without the disturbance of the shadow in image. Experiments have been designed and performed. The experimental results demonstrate the effectiveness of the proposed method. ; This paper presents a new approach to detect and remove the shadows for workpiece localization, which is with an extensive application in automatic assembly system. However, the shadows of workpiece will badly affect this procedure as the contour of the shadow has the same shape with the workpiece itself in the image. The localization system treats the shadow as a part of the workpiece and make incorrect decision. So removing the shadow in the image before localization is meaningful. Our approach use CAD model to estimate the pose of workpiece, and the contour of object can be drawn in the image. Gray and texture features are used to detect and remove the shadow around the workpiece, and the workpiece is localized without the disturbance of the shadow in image. Experiments have been designed and performed. The experimental results demonstrate the effectiveness of the proposed method. ; This paper presents a new approach to detect and remove the shadows for workpiece localization, which is with an extensive application in automatic assembly system. However, the shadows of workpiece will badly affect this procedure as the contour of the shadow has the same shape with the workpiece itself in the image. The localization system treats the shadow as a part of the workpiece and make incorrect decision. So removing the shadow in the image before localization is meaningful. Our approach use CAD model to estimate the pose of workpiece, and the contour of object can be drawn in the image. Gray and texture features are used to detect and remove the shadow around the workpiece, and the workpiece is localized without the disturbance of the shadow in image. Experiments have been designed and performed. The experimental results demonstrate the effectiveness of the proposed method.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14762
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
作者单位	Institute of Automaton, Chinese Academy of Science
推荐引用方式 GB/T 7714	Qin, Zhengke,Zhu, Wenjun,Wang, Peng,et al. Workpiece localization with shadow detection and removing, Workpiece localization with shadow detection and removing, Workpiece localization with shadow detection and removing, Workpiece localization with shadow detection and removing, Workpiece localization with shadow detection and removing[C],2013, 2013, 2013, 2013, 2013.

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shadow removement.pd（1341KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载