一种基于核密度估计的图像边缘检测方法  

Image Edge Detection Method Based on Kernel Density Estimation

作　　者：周建 徐海芹[1] ZHOU Jian, XU Hai-qin(College of Information Sciences and Technology, Donghua University, Shanghai 201620, Chin)

机构地区：[1]东华大学信息科学与技术学院,上海201620

出　　处：《计算机科学》2018年第B06期239-241,共3页Computer Science

摘　　要：进行图像边缘检测的算法有很多种,其中基于Sobel算子、Laplace算子、Canny算子等的图像边缘检测方法当属经典。但所提方法不同于这些差分算子方法,而是对灰度图像素进行小窗口区域的核密度估计,从而得到一幅核密度图,然后通过核密度图,选择出合适的带宽或阈值来控制图像边缘的检出。实验表明该方法可行且简单快速。There are many algorithms for image edge detection.Among them,the image edge detection algorithms based on Sobel operator,Laplace operator and Canny operator are classic.But the proposed method is different from these differential operator methods.Pixel carries out kernel density estimation in the small window area to obtain a kernel density map.Then the kernel density map is used to select the appropriate bandwidth or threshold to control the image edge detection.Experimental results show that this method is feasible,simple and fast.

关 键 词：图像边缘检测 核密度估计 带宽 阈值 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]