基于中智理论与方向α-均值的图像边缘检测算法

Image edge detection based on intelligence theory and direction α-mean

为了提高边缘检测算法对目标边缘细节的保持能力和降低噪声导致的伪边缘等问题,设计了一种基于中智理论与方向α-均值的边缘检测方案。首先,基于中智理论,将图像转换为中智图像,通过真实性、不确定性和虚假性3个要素来表示中智图像,提高了噪声等不确定性信息的表达能力;然后,为了有效地去除了噪声并保持边缘细节,计算中智图像像素的方向掩模,并通过方向平均函数定义了一种方向α-均值算子,并利用生成的方向α-均值算法对图像进行各向异性滤波;最后,构建了一种迭代方程,通过判断梯度的阈值来确定图像像素是否为边缘像素,从而完成边缘检测。实验表明,与当前流行的边缘检测算法比较,所提方法能够更为准确地检测出目标边缘,在不同噪声水平干扰下,其检测结果中所含的伪边缘与不连续边缘信息更少。

In order to improve the preservation of edge details and reduce false edges caused by noise in edge detection algorithm, an edge detection scheme based on the theory of Intelligence and direction α-mean was designed. Firstly, based on the theory of Chi-Chi, the image is transformed into intelligence image, and the intelligence image was represented by three authenticity T, uncertainty I and false F members, which improves the expression ability of uncertain information such as noise. Then, in order to remove the noise effectively and keep the edge, the direction mask of the pixel was calculated, and a direction-mean operator was defined by the direction average function. Then anisotropic filtering was performed on the image using the generated direction-mean algorithm. Finally, an iteration equation was defined to determine whether a pixel was an edge pixel by judging the threshold of gradient. Experiments show that the proposed method can detect edges effectively and accurately compared with current popular algorithms. It can eliminate the influence of noise at different noise levels, reduce the generation of false edges and discontinuous edges, and provide a good basis for future industrial automation and intellectualization.