Segmentation of early fire image of mine using improved CV model

Affiliation of Author(s):电子信息工程学院

Journal:Zhongguo Kuangye Daxue Xuebao

Abstract:It is difficult to utilize traditional active contour models to extract the fire region of the early fire image of mine accurately. Aiming at this problem, an improved CV model was proposed. On the basis of calculating the global region fitting centers of the object and background regions, the local gray histograms of the region inside and outside the curve were utilized to obtain the local region fitting centers of the object and background regions. Then the normalized adjustable ratios were incorporated into the global and local region fitting centers, which can synthetically utilize both global and local information of the image. To accelerate the motion of the curve towards the object boundaries, the minimum absolute differences of the pixel grayscale values inside and outside the curve were used to replace the original internal and external energy weights, which can improve the segmentation efficiency of the model. The experimental results show that, compared with CV model, LBF model, LGIF model, and the CV model incorporating adaptive energy weight, the proposed model can extract the fire region of the early fire image of mine more rapidly and accurately, and has obvious advantages in terms of segmentation performance and segmentation efficiency. © 2018, Editorial Board of Journal of CUMT. All right reserved.

ISSN No.:1000-1964

Translation or Not:no

Date of Publication:2018-03-01

Co-author:韩斌,倪康

Correspondence Author:wyq

Date of Publication:2018-03-01