imgkmeans

  将K均值算法用于图像分割,输入的是彩色图像,转换为灰度图像进行分割,输出结果为灰度图像.利用灰度做为特征对每个像素进行聚类,由于光照等原因,有时应该属于一个物体的像素,其灰度值也会有很大的差别,可能导致对该像素的聚类发生错误.在分割结果中,该物体表面会出现一些不同于其它像素的噪声点,因此,算法的最后,对结果进行一次中值滤波,以消除噪声,达到平滑图像的作用(The K means algorithm for image segmentation, the input is a color image, convert to grayscale image segmentation, the output of grayscale images. The use of gray as the characteristics of each pixel clustering, due to light and other reasons, and sometimes should belong to an object pixel, its gray value will also be very different, may lead to clustering of the pixel error has occurred. in the segmentation results, the surface, there would be different from other pixel noise points, so , the algorithm Finally, the results of a median filter to eliminate noise, to the role of smoothing the image)

imgkmeans
.........\corelread.m
.........\DistMatrix.asv
.........\DistMatrix.m
.........\kMeansCluster1.asv
.........\kMeansCluster1.m
.........\readsize.m
.........\result
.........\......\10.fig
.........\......\113044.fig
.........\......\23084-medfilt2.fig
.........\......\23084-medfilt2[7,7].fig
.........\......\23084.fig
.........\rgb2lab.m
.........\show_seg_img.asv
.........\show_seg_img.m
.........\test




.........\Untitled3.asv
.........\Untitled3.m
.........\利用k均值进行图像分割.doc