基于次序统计滤波器的阶跃边缘检测

在二值图像的边缘检测理论中，经典的方法是用固定的卷积核构成边缘检测算子，如Sobel算子，Prewitt算子，Kirsch算子和Roberts算子等。它们对数字图像边缘的复杂几何结构缺乏算法自适应性。现作者将随机滤波的思想引入边缘检测，用次序统计滤波器构造随机卷积核，从而引入一种新的随边缘检测算子，将OSF边缘算子，并分别对雷达图像和文本图像实施边缘检测。实验结果证明了OSF边缘算子的有效性。

Detection of Step-Structure Edge Based on Order Statistic Filter

In the theory of edge detection of binary image, the classical method is to use regular convolution kernel to construct edge detect operator, such as Sobel operator, Prewitt operator, Kirsch operator and Roberts operator etc. However, these operators for the complex geometric structure of digital image edge lack algorithm adaptability. The authors propose stochastic filtering to edge detection. Using order statistic filter to construct stochastic convolution kernel, the authors yield a new kind of stochastic edge detect operator OSF edge operator. The authors carry out the edge detection for radar images and document images, and the experimental results show the efficency of the OSF edge operator.