基于改进Lazy Snapping算法的红外图像分割方法研究

针对红外图像含大量噪声以及对比度低等特点,提出一种结合快速模糊C均值聚类的改进Lazy Snapping分割方法.对红外图像使用快速模糊C均值聚类算法进行预分割,通过形态学骨架提取的方法在图像中标记出目标和背景种子点,将Lazy Snapping算法由全局分割转化为聚类区域分割,并构造能量函数,通过最小割算法求解能量函...

An Infrared Image Segmentation Method Based on Improved Lazy Snapping Algorithm

Considering that infrared images contain a considerable amount of noise and are of low contrast,an improved lazy snapping(LS)segmentation method combined with fast fuzzy C-means clustering is proposed.Infrared images are pre-segmented using a fast fuzzy C-means clustering algorithm,and the target and background seed points are marked in the image by the morphological skeleton extraction method.The LS algorithm is converted from global segmentation to cluster region segmentation,and an energy function is constructed.The minimum value of the energy function is solved by the minimum cut algorithm,and the segmentation efficiency is improved.The phenomenon of over-segmentation in the image is reduced,the LS algorithm is changed from an interactive algorithm to a non-interactive algorithm.Thus,the automatic segmentation of infrared images is realized,improving the real-time nature of the LS algorithm.By performing segmentation experiments on various infrared images and then comparing the proposed method’s performance with that of other segmentation methods,the results show that the improved algorithm has a good segmentation effect and strong robustness.