An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation

Abstract

When using image processing technology to analyze mineral particle size in complex scenes, it is difficult to separate the objects from the background with traditional algorithms. This paper proposes an ore image segmentation algorithm based on a histogram accumulation moment, which is applied to multi-scenario ore object location and recognition. Firstly, the multi-scale Retinex color restoration algorithm is used to improve the contrast in the dark region and eliminates the shadows generated by the stacked adhesion ores. Then, the zero-order and first-order cumulative moments close to the selected gray level are calculated, reducing the error caused by noise. Finally, the selected gray level gradually approaches the optimal threshold to avoid falling into local optimum. It can segment mineral images with unimodal or insignificant bimodal characteristic histogram effectively and accurately. Ore images in three different scenarios are used to verify the accuracy and effectiveness of the proposed method. The experimental results demonstrate that the proposed algorithm provides better segmentation results than other methods.