- https://doi.org/10.1109/rcar.2018.8621715
An Object Segmentation Method Based on Image Contour and Local Convexity for 3D Vision Guided Bin-Picking Applications
- Aug 1, 2018
- Yunlian Song +4 more
Abstract
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.
- # Local Convexity
- # Dimensional Image Processing
- # Bin-picking System
- # Point Cloud Segmentation
- # Dense 3D Point Cloud
- # Image Contour
- # Object Segmentation
- # Dimensional Point Cloud
- # Point Cloud
- # Dense 3D Point
