FPCM算法在彩色图像分割中的方法研究

在改进模糊c均值聚类（MFCM）算法基础上提出模糊可能性c均值聚类（FPCM）算法的图像分割方法，并将FPCM算法应用在彩色图像分割过程中．FPCM算法是FCM聚类算法和MFCM算法的扩羼衍生．MFCM算法是通过调整FCM算法的测量距离来减少标签像素受到其他图像像素的影响和在切分中抑制噪声效果来进行约束，从而使得成员变量没有最大约束值．FPCM算法是在MFCM算法基础上进行模糊化，加入了可能性和隶属度两个度量标准．通过彩色图像实验充分表明了FPCM算法在图像分割中的实际效果．

The Research of FPCM Algorithm Method in Colorful Image Segmentation

In this paper, improving algorithm based fuzzy C-means clustering （MFCM） the possibility of fuzzy C-means clustering （FPCM） for image segmentation method, and FPCM algorithm is applied to col- or image segmentation process. FPCM algorithm is extended derivative the FCM algorithm and MFCM of al- gorithm. MFCM algorithm by adjusting the distance of the measurement of the FCM algorithm to allow the label pixels by pixels and noise suppression effect to the constraint in the segmentation, so that the member variable is not the maximum constraint value. FPCM algorithm is in MFCM algorithm based on fuzzy, by adding the possibility of membership of two metrics. FPCM algorithm fully shows that the actual effect of the color image segmentation based on color images of experiments.