数学形态学在图象处理中的应用进展

数学形态学是一种非线性滤波方法，形态和差运算，即膨胀与腐蚀是数学形态学的基础，数学形态学已由二值形态学、灰度形态软数学形态学、模糊形态学发展到模糊软形态学，可用于抑制噪声、特征提取、边缘检测、图象分割、形状识别，纹理分析、图象恢复与重建等图象处理问题，在图象处理领域得到了越来越广泛的应用，本文结合目前的研究进展，对数学形态学的理论研究及其应用进展进行综述性阐述。

The Advances of Mathematical Morphology in Image Processing

Mathematical morphology is a methodology of nonlinear filters. The basic morphological operations which stem from Minkowski set operations are dilation and erosion. Mathematical morphology firstly handled binary images as sets and probed them with a structuring element which formed binary morphology, and then gradually formed gray scale morphology, soft mathematical morphology, fuzzy mathematical morphology, and fuzzy soft mathematical morphology. It has been widely used in the area of image processing such as noise suppression, edge detection, image segmentation, feature extraction, nonlinear image filtering and so on. We briefly review some recent advances both in the theory and applications of morphological image analysis.