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.