工程科学学报2017，Vol.39Issue(12)：1866-1873,8.DOI:10.13374/j.issn2095-9389.2017.12.013
基于改进CV模型的金相图像分割
Segmentation of metallographic images based on improved CV model
摘要
Abstract
The segmentation of metallographic images plays a key role in grain grading, but it is difficult to extract grains accu-rately using the traditional Chan-Vese ( CV) model. To segment metallographic images more accurately, a metallographic image seg-mentation method based on an improved CV model was proposed. First, the level set function was initialized, and its reciprocal Can-berra distance from inside and outside the curve was calculated. Then, these distances were used as weight coefficients of the fitting centers to restrain the influence of noise points on their accuracy. In addition, adding exponential entropy to adjust the energy inside and outside the curve reduces the influence of the fixed energy weight on the evolution of the curve. Lastly, to accelerate the conver-gence of the model, a distance-regularized term was introduced to avoid re-initialization of the level set function. The experimental re-sults show that, compared with the traditional CV model, the geodesic active contour model, the distance-regularized level set evolu-tion model, and the bias level correction level set model, the segmentation of the metallographic images based on the proposed model is more accurate and efficient, and the proposed model has better convergence.关键词
金相图像分割/晶粒评级/Chan-Vese模型/水平集/倒数坎贝拉距离/指数熵Key words
segmentation of metallographic image/grain grading/CV model/level set/reciprocal Canberra distance/exponen-tial entropy分类
矿业与冶金引用本文复制引用
倪康,吴一全,韩斌..基于改进CV模型的金相图像分割[J].工程科学学报,2017,39(12):1866-1873,8.基金项目
国家自然科学基金资助项目(61573183) （61573183）
新金属材料国家重点实验室开放基金资助项目(2014-Z07) （2014-Z07）
