基于红外图像特征与K-means的边缘检测

为解决红外图像边缘模糊导致边缘提取困难的问题,提出一种基于边缘特征与K-means结合的红外图像边缘检测方法。首先将人眼视觉特性与红外图像边缘点处的灰度分布特点结合,构造出反映其结构特征的数据集;再利用K-means将数据集分为边缘点和非边缘点,提取出图像边缘;最后利用二步法将边缘进行细化,以便实现红外图像边缘检测。实验结果表明:该方法能够通过自适应阈值提取出红外图像的完整外部轮廓,并保留内部边缘信息,对弱边缘起到良好的提取效果,并有效抑制噪声干扰。

Edge Detection Based on Characteristics of Infrared Image and K-means

An infrared image edge detection method based on edge characteristics combined with K-means is proposed in this study to solve the problem of edge extraction caused by the blurring of infrared image edges.First,human visual characteristics are combined with gray distribution characteristics at the edge of the infrared image to construct a data set reflecting its structural characteristics.Second,K-means is used to classify the data set into edge and non-edge points to extract the image edges.Third,the edge is refined using a two-step method to achieve infrared image edge detection.The experimental results show that the proposed method can extract the complete external contour of the infrared image through the adaptive threshold and retain the internal edge information,which can extract the weak edge and effectively suppress noise interference.