An Improved Infrared Image Segmentation Method Based on Maximum Between-Class Variance Method

Abstract

The invention provides an improved infrared image segmentation algorithm based on the Otsu method, and the algorithm can solve a problem of poor segmentation effect caused by sky exposure to the greatest extent, and can enable the segmented target to maintain a more complete shape. Because the contrast of an infrared image is low relatively and the gray scale range is relatively small, the Otsu method cannot achieve the good segmentation of a target. Through giving consideration to the impact on the segmentation from the image gray scale, the number of background pixels of an image and the number of target pixels, the algorithm achieves the improvement of a formula for solving a variance in a conventional Otsu method, irons out the defects that an infrared image segmentation algorithm based on the Otsu method cannot obtain a better segmentation result under the condition of sky exposure and smaller contrast between the sky and the target, and enables the segmentation effect to be goodin a region where the contrast between the target and the sky background is larger.

Description

technical field [0001] The invention belongs to the technical field of infrared imaging, and in particular relates to an improved infrared image segmentation method based on the maximum inter-class variance method. Background technique [0002] In the imaging process of the infrared imaging system, due to its own imaging reasons, the infrared image has the characteristics of low signal-to-noise ratio, blurred image, and low contrast. When UUV shoots infrared reconnaissance images near the sea, due to the influence of UUV movement, the difference in temperature of the shooting scene, and the interference of waves, the quality of the infrared images taken by it is worse than that of ordinary infrared images. Because the contrast of infrared images is relatively low and the range of gray levels is relatively narrow, the target cannot be well segmented by using the method of maximum variance between classes. In order to better segment the target, considering the influence of th...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the disadvantage that the infrared image segmentation algorithm of the maximum inter-class variance method in the prior art cannot obtain better segmentation results when the sky is exposed and the contrast between the sky and the target is low, the present invention aims to provide a An Improved Infrared Image Segmentation Method Based on the Maximum Between-Class Variance Method for Segmenting Areas with High Contrast Between Target and Sky Background

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