Industrial Smoke Image Segmentation Based on a New Algorithm of Cross-Entropy Model

Smoke segmentation from the industrial images is a key concern of environmental monitoring. As the similarities between the gray value of the background and the smoke, the existing segmentation algorithms are difficult to accurately segment the target smoke. In this paper, we construct a cross-entropy based industrial smoke image segmentation by integrating the iterative convolution-thresholding. Specially, we use the iterative convolution-thresholding to implicitly represent the interface of each image domain through a characteristic function. We further perform the combination of a regularization term and a fidelity term in the cross-entropy model. In the proposed algorithm, the fidelity term is first converted into the product of the characteristic function and the cross-entropy function. Then the functional of the characteristic function is used to obtain the regularization term by the approach of thermonuclear convolution approximation. The experimental results demonstrate that our proposal has a more accurate segmentation effect and higher segmentation efficiency.