Ceramic Microstructure Image Segmentation by Mean Shift

Abstract

In order to effectively assist the researchers conduct quantitative analysis of ceramic microstructures, a segmentation algorithm based on mean shift is used for the ceramic microstructure image. Since the collection and transfer process of microscopic image will inevitably be subject to uneven distribution of light, electronic noise and other interference factors which make the image quality deterioration, it is necessary to reduce noises and enhance edges for ceramic microscopic image processing at first. Therefore, the median filter is used to remove the noises in the ceramic microstructure images. Then the component with similar feature is separated and merged by the mean shift segmentation algorithm. Experiments show the proposed algorithm of using median filter and mean shift clustering gives satisfactory results.

SEGMENTATION AND ANALYSIS METHOD FOR TWO-PHASE CERAMIC (HfB2-B4C) BASED ON THE DETECTION OF VIRTUAL BOUNDARIES

“…There have been several methods related to the segmentation of the ceramic microstructure. Cai et al (2013) proposed a segmentation method based on mean shift. The method can give the satisfactory results on alumina ceramic images and ceramic tile images.…”

An Improved Mean Shift Segmentation Method of High-Resolution Remote Sensing Image Based on LBP and Canny Features

“…However, traditional Mean shift merely requires the space position and color features as the feature vector ending in the limitation of the ability to improve the segmentation accuracy. While texture features can improve the segmentation [4], thus this paper is about to advance an improved Mean shift segmentation method of high-resolution remote sensing image on account of LBP and Canny features. To study its accuracy, this paper compared the improved and the traditional Mean shift in terms of remote sensing image segmentation results.…”

Research on Image Segmentation Algorithm Based on Entropy and PSO Algorithm

“…According to the different characteristics of the image, image segmentation as a kind of target extraction technology, can divide the image into several areas, one or several of which are the targets [1]. The characteristics may be color, grayscale, texture, and so on.…”