铁道标准设计2018，Vol.62Issue(4)：52-56,5.DOI:10.13238/j.issn.1004-2954.201705150005
基于灰度对比图与最大熵的钢轨图像分割
Image Segmentation Based on Gray Contrast and Maximum Entropy
摘要
Abstract
Due to the uneven gray level, overexposure and excessive noise of collected images, the one-dimensional Maximum Entropy can not accurately segment the defects of the rail images. This paper presents an image segmentation algorithm based on gray contrast and morphological reconstruction and the Maximum Entropy to segment images. Firstly, the gray contrast image of rail images is obtained. Then the gray contrast image is reconstructed by morphological reconstruction, and the reconstructed image is subtracted by gray contrast image to get the difference graph containing the defects. Finally, the difference graph is segmented by the Maximum Entropy. The experimental results show that the gray contrast image proposed in this paper can well alleviate the effects on detection caused by uneven illumination and overexposure, and the morphological reconstruction can not only obtain the desired background model but also suppress the noise. This algorithm is simple, effective and robust, and the segmentation accuracy can reach up to 90%.关键词
钢轨/表面缺陷/过度曝光/最大熵/形态学重构/灰度对比图Key words
Rail/Surface defects/Overexposure/Maximum Entropy/Morphological reconstruction/Gray contrast分类
信息技术与安全科学引用本文复制引用
李晓梅,顾桂梅,常海涛..基于灰度对比图与最大熵的钢轨图像分割[J].铁道标准设计,2018,62(4):52-56,5.基金项目
甘肃省科技研究计划项目(1508RJZA059) （1508RJZA059）
