计算机与现代化

• 算法设计与分析 • 上一篇    下一篇

基于直线先验知识的工件缝隙水平集分割方法

  

  1. 1.同济大学电子与信息工程学院计算机科学与技术系, 上海201804; 
    2.同济大学电子与信息工程学院CAD研究中心, 上海201804
  • 收稿日期:2015-08-20 出版日期:2016-01-22 发布日期:2016-01-26
  • 作者简介:陈启明(1990-),男,浙江绍兴人,同济大学电子与信息工程学院计算机科学与技术系硕士研究生,研究方向:计算机视觉; 王志成(1975-),男,上海人,副研究员,博士,研究方向:仿真与多媒体。

Weld Seam Level Set Segmentation Method Based on Straight Line Prior Knowledge

  1. 1. Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China;
    2. CAD Center, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
  • Received:2015-08-20 Online:2016-01-22 Published:2016-01-26

摘要: 焊缝识别和分割是实现自动化焊接的重要环节。点焊过程产生的焊疤及药皮燃烧产生的喷射物,会在分割图像中产生严重噪声,从而影响焊缝分割的效果。考虑到实际应用中焊缝呈现直条型,本文提出一种基于直线先验形状信息的水平集分割方法,将缝隙的曲率变化作为先验信息正则化主动轮廓模型,过滤掉伪焊缝,提高了算法的鲁棒性。与传统的CV模型和改进的LBF模型相比,本文所提方法具有更好的分割效果和更高的准确率。

关键词: 焊缝分割, 水平集, 先验知识

Abstract:  Weld seam identification and segmentation is an important part of automated welding. The burr produced during spot welding process will bring serious noise in the image, thus affecting the weld segmentation results. Taking the shape of weld seam into account, the paper proposed a weld seam level set segmentation method based on straight line prior information. Since the weld seam is always a straight bar, the proposed method takes the curvature of weld seam as a prior information regularization term, and added it to the active contour models. This is very useful to filter out pseudo-seam, thus it improves the robustness of the algorithm. Compared with the traditional CV model and the improved LBF model, the proposed method is of better segmentation results and higher accuracy.

Key words:  weld seam segmentation, level set, prior knowledge

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