图像多尺度秩和统计间隙的模糊边缘检测模型

A Multi-Scale Images Edge Detection Model Based on Gap Statistic of Order Wilcoxon Rank Sum

基于“Gap统计”理论思想,在概念“Wilcoxon秩和统计量”基础上提出了顺序秩和统计量、顺序秩和间隙以及边缘隶属度的概念,以相对半邻域之间图像灰度分布的顺序秩和差别为依据,建立了基于顺序秩和统计间隙的多尺度图像模糊边缘检测模型,分析了模型与Prewitt算子的关系·通过图像的边缘检测实例对模型进行了验证,比较了不同尺度下边缘检测的差别,验证并分析了噪声对边缘影响依尺度的关系·该模型具有多尺度、区域内部抗噪能力与尺度有关、噪声弱化弱边缘以及边缘具有隶属度、图像的边缘检测结果与尺度有关等特点,反映了边缘与纹理依尺度的关系·

On the basis of the order Wilcoxon rank sum statistics in relative half-neighborhood of image pixels, concepts called order Wilcoxon rank sum statistics, statistic gap of order Wilcoxon rank sum, and membership degree of an image edge are proposed. Based on the gap statistic of order Wilcoxon rank sum, a multi-scale images edge detection model is established. Because the proposed model has the property of multi-scale, the excellent anti-noise in the interior of the region, and the noise weakening faintish edge, the corresponding algorithm has good performance in image edge detection. The difference of edge detection between the model and Prewitt operator is analyzed. Lots of experimental examples verify the capacity of the model. Finally, an investigation is taken to compare the difference of edge detection under the circumstance of different scales.