为了防止分水岭算法过分割问题，研究了一种基于形态处理和纹理分析的图象分水岭分割方法，该方法是在分水岭算法的基础上，首先对形态梯度图象运用形态开闭滤波器组的方法来获得较好的参考图象，然后将提取的二值化初始分割结果作为区域标记来进行分割.另外，为了获得整体目标，还定义了一个基于纹理特征的一致性和对比度的检验准则，并将其作为区域合并的根据.该方法应用于多个视频序列初始目标分割的结果表明，形态滤波器组的引入很好地防止了过分割，并证明基于纹理特征均匀性和对比度的准则对分割区域进行进一步的纹理合并是行之有效的.

This paper suggested a target segmentation scheme based on morphological processing and texture analysis. Firstly, an opening-closing filter bank performed on the gradient image for watershed segmentation is proposed. The filter bank concerns with eight structure elements, each is selected to represent the allowed neighbor patterns and used to generate a reference image for segmentation. This reference image is more suitable than the gradient one when calculating the watersheds, because its main structures are preserved while pseudo-contours are discarded. Then binary marker extraction is carried out to get initial partition. These region markers mark the relevant objects and backgrounds to act as the minima set of segmentation, the initial object boundaries are finally obtained by computing the watersheds. Secondly, a criterion with respect to the homogeneity and the contrast of textures is defined to merging regions, it weighs the contrast of two neighbor patterns and adapts itself to different sizes of neighbor patterns. Experimnts on several video sequences are presented to demonstrate that the morphological filter bank could prevent the result from over-segmentation and the criterion is also validity.