基于多分辨率分析及QFCM算法的图像分割方法研究

RESEARCH ON IMAGE SEGMENTATION APPROACH BASED ON MULTIRESOLUTION ANALYSIS AND QFCM ALGORITHM

在图像的多分辨率小波分析的基础上 ,采用高斯 -马尔可夫随机场模型来描述图像的局部特征 .利用 L MS算法 (the least- mean- square algorithm )求得模型的参数估计 ,构造出图像的特征集 .再利用快速模糊 C-均值聚类方法 (QFCM)对该特征集进行模糊划分 ,从而完成图像的分割 .实验证明 ,这种方法具有较强的适应性 ,尤其对于景物 -背景对比度差以及信噪比较低的一类图像 ,具有良好的分割效果 .

The hierarchical multiresolution wavelet analysis in conjunction with the contextual information of the image extracted from GMRF results in local features of the image. The model parameters are estimated by the LMS algorithm, and these parameters constitute a feature set of the image. The segmentation is then implemented via fuzzy partitioning of the feature set with the QFCM algorithm. The adaptiveness of the approach has been tested by simulations on a wide range of images. The experiment shows that this technique is especially effective for those images with low object surrounding contrast or images with low SNR.