Multi-scale Edge Detection Model for Images Based on Grayscale Gap
图像边缘检测的多尺度灰度Gap统计模型

On the basis of the gray-level distribution in the relative half-neighborhood of image pixels,refering to the method called "Gap statistic" proposed by Hastie and Tibshirani,a concept called inverse distribution function is brought forward,and a multi-scale edge detection model based on Gap of random variable was established.By analyzing consistency between a grayscale distribution Gap and random variable Gap,the edge detection algorithm for Gap statistic model is optimized.The paper analyzes the correlation between the Gap statistic model and two operators(Prewitt operator and Sobel operator),discusses the anti-noise and multi-scale properties of the edge detection model,and an investigation is made to analyze the difference of edge detection at different scales.Finally,experimental examples verify the capacity of the model.