粗糙集理论在阴影图像边缘检测中的应用

Rough Sets Used In The Field of Shadow Image Edge Detection

粗糙集理论是一种新的处理模糊和不确定性问题的数学工具,该文提出一种基于粗糙集阴影边缘方法,该算法根据粗糙集理论、梯度、最大邻域差及噪声的条件属性,将一幅图像划分为不同的子图像,然后对子图像分别进行处理,得到阴影边缘点,再对边缘点进行细化和跟踪,删除那些假边缘点,最后得到阴影边缘图像。通过对所得结果进行分析可知,结合粗糙集理论的阴影图像边缘检测算法与其他的常规检测方法相比,无论从视觉效果还是检测精确度上都得到了改善。

The theory of the Rough sets is a new mathematics tool which used to process fuzzy and indetermination problem,this paper put forward a new method of the shadow edge detection based on Rough sets,which according to the theories of the Rough sets and the condition attribute of the gradient,the biggest error of neighborhood and the noise.The method divides a picture into the different several sub-pictures, then respectively process the sub-picture and get the shadow edge points.Then these edge points are thinned and followed and those false edge points are deleted. Finally,shadow edge images are obtained.It draws the conclusion by analyzing the result:the detecting accuracy and visual effect are improved by comparing the edge detection method based on rough set with normal methods.