Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Chinese Journal of Engineering》 2017-12
Add to Favorite Get Latest Update

Segmentation of metallographic images based on improved CV model

NI Kang;WU Yi-quan;HAN Bin;College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics;State Key Laboratory for Advanced Metals and Materials,University of Science and Technology Beijing;  
The segmentation of metallographic images plays a key role in grain grading,but it is difficult to extract grains accurately using the traditional Chan-Vese(CV) model. To segment metallographic images more accurately,a metallographic image segmentation method based on an improved CV model was proposed. First,the level set function was initialized,and its reciprocal Canberra 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 convergence of the model,a distance-regularized term was introduced to avoid re-initialization of the level set function. The experimental results show that,compared with the traditional CV model,the geodesic active contour model,the distance-regularized level set evolution 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.
【Fund】: 国家自然科学基金资助项目(61573183);; 新金属材料国家重点实验室开放基金资助项目(2014-Z07)
【CateGory Index】: TG115.21
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved