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

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 str...

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Bibliographic Details
Published in2018 IEEE International Conference on Real-time Computing and Robotics (RCAR) pp. 474 - 478
Main Authors Song, Yunlian, Wu, Shiqian, Zhao, Juan, Gu, Feifei, Song, Zhan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
bin-picking system
Filling
image contour
Image segmentation
Manipulators
Object segmentation
structured light
Three-dimensional displays
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Summary: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.
DOI:10.1109/RCAR.2018.8621715