基于AOS的扩展C-V模型及背景填充耦合的单板节子缺陷识别

分析木材单板节子缺陷图像的特点,提出一种基于AOS的扩展C-V模型及背景填充耦合的单板节子缺陷识别算法。首先,对Chan-Vese提出的基于Mumford-Shah模型的水平集图像分割算法进行改进,使分割速度得到提高;其次,用AOS算法改进原模型的差分格式,使得差分格式无条件稳定;最后,结合背景填充技术,使得到的新图像缩减了目标与背景间的特征差别。通过对比试验,表明该分割方法能够快速识别单板单个节子缺陷,充分说明该耦合方法比Chan-Vese方法及其改进方法有更好的分割效果。通过用多水平集作为初始轮廓演化曲线,结果表明该方法也可快速实现对单板多节子缺陷图像的识别,实现对单板节子图像的多目标分割。

An Inspection for the Veneer Knot Defect Based on Extension of C-V Model and AOS Scheme Coupling with Technique of Painting Background

The features of the defects image of wood veneer are analyzed,an algorithm of veneer knot defect inspection is proposed which based on extension of C-V model and AOS scheme coupling with filling background.Firstly,improved a new level set approach for image segmentation which was proposed by Chan-Vese based on Mumford-Shah,the algorithm could increase the speed of segmentation.Secondly,the approach used AOS scheme,which is unconditional stable,to discrete the level set function.Finally,coupling with the tec...