DENG Wenjing，ZHOU Wu，CAI Xiaoshu

(School of Energy and Power Engineering; Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering,University of Shanghai for Science and Technology, Shanghai 200093, China)

Abstract: Color images of core particles contain complex and diverse information. In addition to the color and spatial shape features easily perceived by human visual system, deeper texture feature information is also implied. In this paper, a clustering segmentation algorithm of Kernel Fuzzy C-Means (KFCM) was proposed based on multi-dimensional features. Firstly, the color images of core particles were convoluted by Gabor filter banks at different scales and directions in the frequency domain, and the results of Gabor filtering were used as local texture features of the spectrum. Then, texture features, color features and spatial location information of image pixels were merged into the Kernel Fuzzy C-Mean clustering algorithm to realize color image segmentation of core particles. The results show that compared with the segmentation results of other algorithms, the multi-dimensional feature KFCM clustering segmentation algorithm can more accurately identify the color images of different types of core particles and obtain good segmentation results.

Keywords: core particle; color image segmentation; Gabor texture feature; Kernel Fuzzy C-Means clustering algorithm; multi-dimensional features

中图分类号：TP319.41                            文献标志码：A

文章编号：1008-5548(2019)06-0012-07

DOI:10.13732/j.issn.1008-5548.2019.06.003

收稿日期： 2019-05-29， 修回日期：2019-07-01，在线出版时间：2019-09-30 09:19。

基金项目：国家自然科学基金项目，编号：51576130。

第一作者简介：邓文晶(1995—)，男，硕士研究生，研究方向为图像处理与颗粒测量。E-mail：wenjingdeng@126.com。

通信作者简介：周骛(1985—)，女，副教授，硕士生导师，研究方向为图像法颗粒与流场测量。E-mail：zhouwu@usst.edu.cn。