摘要
为解决Chan-Vese模型和局部二元拟合(Local binary fitting,LBF)模型在带钢缺陷图像分割时存在的对初始轮廓位置敏感、运行速度较慢等问题,提出引入局部信息的带钢缺陷图像凸优化活动轮廓分割模型(Local information convex activecontour,LICAC)。该模型利用凸优化技术将一个非凸的分割模型转变为凸优化问题,并采用Split Bregman方法对问题进行快速求解,从而解决Chan-Vese模型和LBF模型对初始轮廓位置敏感等问题。通过引入图像局部信息,该模型可以有效分割灰度不均匀的带钢表面缺陷图像。使用该模型分别对焊缝、黄斑、孔洞和划伤等4大类单个带钢缺陷目标区域的图像进行分割试验,分割效果和运行时间都明显优于其余两种模型。同时,该模型也可用于含多个缺陷目标区域的图像分割,并通过对划伤、夹杂、麻点和抬头纹等4大类常见的多个缺陷目标区域的图像进行分割试验,验证了该模型的有效性。
In order to solve problems existing in Chan-Vese model and local binary fitting （LBF） model, such as model sensitivity to the initial contour position and running slow in the segmentation of strip steel defect image, a novel model local information-based convex active contour （LICAC） is proposed. By converting non-convex optimization problem to a convex optimization problem via convex optimization technology, and applying the Split Bregman method for fast solution, the issues of the sensitivity to the initial contour position occurring in Chan-Vese model and LBF model are solved. With introduction of the local information, the new model is efficient in the segmentation of the strip surface defect image which is non-uniform gray. By using this model to segment single-target region strip defect image, four common defect categories, including weld, rust, holes and scratches are experimented, and experimental results show that the segmentation effect and operation time of the proposed model are better than the rest two kinds. In addition, this model can also be used to segment multi-target regions defect image, four common defect categories are experimented, including scratches, inclusion, pitting, and wrinkles, and experimental results have verified the validity of the model.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2012年第20期1-7,共7页
Journal of Mechanical Engineering
基金
国家高技术研究发展计划资助项目(863计划
2008AA04Z135)
