Noise of uneven ore objects in images is seriously influenced by illumination and textures, causing it cannot be accurately and effectively segmented by traditional threshold method. The calculation cost of the double-Window OTSU method is too high, and the extreme value of the double-Window threshold is taken as the optimal threshold value of the target pixel, resulting in partial pixel misclassification. In this paper, a double-window OTSU algorithm based on binomial distribution optimization was presented, the size of the double window template depended on the maximum and minimum target sizes. The feasibility and accuracy of the proposed method were proved by experiment and mathematical theory, and the time cost was reduced by 50%~80% greatly. The classification accuracy was improved by about 6% for the image of object easy classification, about 13% for the image of object uneasy classification. The algorithm determined the size of the window independently, increased its intelligence and strong anti-noise, reduced the misclassification rate and greatly reduced the time cost. |