激光与光电子学进展, 2021, 58 (2): 0210014, 网络出版: 2021-01-08
基于区域灰度极小值的网孔织物图像分割算法 下载: 825次
Image Segmentation Algorithm of Mesh Fabric Based on Regional Minimum Gray Value
图像处理 图像分割 局部极值点 图像融合 网孔织物 图像增强 image processing image segmentation local extreme point image fusion mesh fabric image enhancement
摘要
针对网孔织物图像的对比度低和噪声点多而导致分割结果中存在网孔连在一起和残缺等问题,提出一种基于区域灰度极小值的分割算法以期提高网孔的分割精度。首先利用高斯金字塔缩放和直方图均衡化算法处理图像以增强图像的纹理轮廓和明暗对比度。然后采用一种基于区域灰度极小值的分割算法以解决仅仅依靠灰度值大小而无法正确分割网孔的问题。最后采用一种多图像融合算法以解决基于局部灰度极小值的分割算法中阈值选择困难的问题。选择多种不同光照程度的网孔织物图像进行实验,实验结果表明所提算法的分割效果良好,能够有效地解决分割结果中网孔连在一起和残缺等问题,且网孔织物的分割错误率为0.24%。
Abstract
In view of the low contrast and many noise points of the mesh fabric image, the segmentation results have the problems of mesh connection and incompleteness. A segmentation algorithm based on the minimum gray value of the region is proposed to improve the segmentation accuracy of the mesh. First, the image is processed with Gaussian pyramid scaling and histogram equalization algorithm to enhance the texture contour and contrast of light and dark of the image. Then, a segmentation algorithm based on the minimum gray value of the area is used to solve the problem that the mesh cannot be segmented correctly only by the gray value. Finally, a multi-image fusion algorithm is used to solve the problem of difficult threshold selection in the segmentation algorithm based on local gray scale minima. A variety of mesh fabric images with different illumination levels are selected for experiments. The experimental results show that the proposed algorithm has a good segmentation effect, which can effectively solve the problems of mesh adhesion and incompleteness in the segmentation results. The segmentation error rate of the mesh fabric is 0.24%.
化春键, 孙康康, 陈莹. 基于区域灰度极小值的网孔织物图像分割算法[J]. 激光与光电子学进展, 2021, 58(2): 0210014. Chunjian Hua, Kangkang Sun, Ying Chen. Image Segmentation Algorithm of Mesh Fabric Based on Regional Minimum Gray Value[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210014.