数学形态学在图象处理中已经得到广泛地应用，但传统的形态学常应用于二值图象处理，后来发展应用到灰度图象处理，对于其用于彩色图象处理的研究还不是很多，通过对传统的数学形态学的几何描述，以及对目前形态学在边缘检测中的应用分析，提出了一种新的多尺度的彩色形态矢量边缘检测算子，该方法是利用不同尺度形态边缘检测算子来检测不同尺度下的边缘强度，再对不同尺度下的边缘强度图进行合并，从而得到新的边缘强度图象，利用该算法对实际图象和合成图象进行了实验，将实验结果与传统的边缘检测算法相比较，由于新的多尺度彩色形态矢量算子能检测出更多的细节边缘，因此将更有利于图象的进一步分析处理，同时将实验图象人为地增加噪声后，再利用该算法进行实验，其结果表明，该算法对噪声具有很好的鲁棒性。

Mathematical morphology in its original form is a set-theoretical approach to image analysis. It studies image transformations with a simple geometrical interpretation and their algebraic decomposition and synthesis in terms of elementary set operations. Mathematical Morphology has been applied in many fields, at the beginning, it is only applied in binary images called binary-scale morphology, and then it has been developed to gray-level images called gray-scalemorphology, but there are few researches in color image. In this paper we present a new color vector morphological edge detection methods using a multi-scale approach for detection edge under noisy condition. The goal of edge detection is to detect and localize edge points even under noise condition. Not all edges with various fineness regarding spectral contrast and spatial geometry can be detected by a single operator. In fact, some details that seem to be freak and noisy in one scale may become relevant in other scale. Hence, edges of different fineness are detected using operator at different scale, and then they are judiciously combined to produce all the edges of interest in an image. The experiment has proved this proposed method can detect detail edges in color image. Its superiority has been revealed by comparing with the traditional edge detection methods such as LOG. The experimental results have shown this method is robust to noise.