Infrared Image Contour Segmentation Model Combining the Signed Pressure Force and Kernel Difference

To address the issue of low contrast between targets and backgrounds in infrared images, which makes it difficult to extract target contours accurately, a novel infrared image active contour segmentation method is proposed that integrates the signed pressure force and kernel difference. First, an optimal kernel difference operator was employed to capture the boundary information from the infrared images. The signed pressure force was then fused with global and local image features, minimizing the energy function through the variational level set method and iteratively updating the level set via gradient descent flow. Second, the fused signed pressure force was combined with the kernel difference operator to update the active contour position, ensuring that the active contour aligned closely with the target edges. Finally, the level set function was evolved using a Gaussian filter to obtain the segmentation results. The experimental results demonstrate that the proposed infrared image contour segmentation model achieved an average accuracy of 92.93%, average recall rate of 93.87%, and average Dice similarity coefficient of 0.904.