基于清晰度的彩色图像分割改进算法

摘要

针对 K-means 算法不能很好的分割目标较小，且目标颜色不能明显区别于背景颜色的彩色图像等缺点，提出了一种基于清晰度的彩色图像分割算法（S-K-means）。该算法引入图像清晰度评价理论，将彩色图像 R、G、B 三个通道的灰度矩阵转换成清晰度矩阵，然后选用Lab 彩色空间，最后进行 K 均值聚类。为验证算法的有效性，对多幅电力线图像进行了分割实验，并与其他的分割方法进行比较实验，给出了详细的理论分析。实验结果表明该算法效果好，对电力系统中高压巡线具有参考价值，且具有较高的使用价值。%Because the K-means algorithm can't segment small targets in color images well,especially the images whose target color can't significantly distinguish from the background color.An improved color image segmentation algorithm based on sharpness (S-K-means)is proposed.The image sharpness evaluation theory is introduced and gray matrixes of R,G,B color channels are transformed into three sharpness matrixes,then they are converted into Lab color space.At last blocks are classified by K-mean clustering method.The new algorithm is applied to segment a number of power line images and compared with other segmentation methods by experiments in order to verify the effectiveness of the proposed algorithm.At the end,both theoretical analysis and experimental results are given.Experimental results show that the effect of this algorithm is better,the conclusion can provide a reference for high voltage cable patrol in e-lectric power system,and this algorithm has high availability.