一种新的局部分水岭模型在图像分割中的应用

为了提高图像分割算法对图像显著区域的抓取能力及效率，将超像素思想与分水岭算法相结合，并且在模糊C均值聚类算法（Fuzzy C-means，FCM）的基础上进行改进，提出了一种基于网格化局部分水岭的模糊聚类算法。该方法先根据区域方差将图像进行不均匀网格化，再对每个网格使用局部最优阈值的分水岭算法，减少了全局分水岭带来的局部信息遗失，获得各个网格内的显著性聚水盆，再实施区域融合，将每个标记区域的灰度均值化，最后使用考虑区域面积的FCM进行聚类，得到最终的分割图像。实验结果表明，该算法对噪声的鲁棒性强，能够有效剔除干扰区域，分割出图像中的显著区域，同时也具有较低的时间复杂度。

Application of New Local Watershed Model in Image Segmentation

In order to improve the ability of image segmentation to grasp significant areas, an algorithm based on grid local watershed method and fuzzy C-means (FCM) is proposed by combing super pixel thoughts and watershed algorithm. The algorithm first partition an image into non-uniform grids according to the variance. For each grid, watershed algorithm is applied with the best gradient threshold to reduce the loss of local information in global watershed. In this way, the significant basins of each grid are extracted. Through regional integration and mean normalization of each area, FCM clustering considering the size of each region is used to get the final segmentation image. Experimental results show its great robustness against noise. In addition, the algorithm can effectively eliminate the interference regions and segment the significant regions of images with a low time complexity.