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An Object Segmentation Method Based on Image Contour and Local Convexity for 3D Vision Guided Bin-Picking Applications

机译:基于图像轮廓和局部凸度的对象分割方法在3D视觉引导拾箱应用中的应用

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Segmentation of targets from a set of disordered objects is always plays a significant role in the field of computer vision. In this paper, a novel method of object segmentation of scattered parts, of which dense and accurate 3D point cloud can be obtained by visual measurement technology of the structured light, is proposed and confirmed to be valid without training large datasets. The randomly placed parts are almost separated completely after two dimensional image processing and point cloud segmentation using local convex convexity connections. The segmentation results can guide the grabbing work of robot arms in the bin-picking system.
机译:从一组混乱的对象中进行目标分割在计算机视觉领域中始终发挥着重要作用。本文提出了一种新的散乱对象分割方法,该方法可以通过结构光的视觉测量技术获得密集,准确的3D点云,并且无需训练大数据集就可以证明其是有效的。经过二维图像处理和使用局部凸凸连接的点云分割后,随机放置的部分几乎完全分离。分割结果可以指导垃圾箱拣选系统中机器人手臂的抓取工作。

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