No abstract

To improve the foreground segmentation and location accuracy of complex coal gangue images with gray histogram distribution close to the unimodal shape, a contour detection algorithm of the grayscale fluctuation matrix is proposed. The contour and non-contour pixels of coal and gangue images are investigated, and the result indicates that the gray values of the pixels around the contour exhibit the non-uniform distribution, and the gray value changes in different directions are significantly different. Accordingly, a grayscale fluctuation matrix is built by calculating the change amplitude of pixels in different directions, and multiple features are extracted from the grayscale fluctuation matrix to realize the target contour segmentation. Furthermore, the contour is optimized using the historical and future information of the contour image, thus effectively removing numerous false contours, reproducing some hidden contours and increasing segmentation accuracy. Compared with the artificial segmentation results, the pixel area and centroid coordinates of the proposed method achieve the maximum error rates of 4.404% and 3.18%, respectively, all lower than 4.5%. This study provides a feasible solution to the edge detection and segmentation of images with similar and complex backgrounds.

To reduce the influence of material particle size on coal gangue identification, a particle size identification method, and an adaptive image enhancement method are proposed, which can accurately identify the particle size of poorly segmented and mutually blocked materials, effectively reduce the reflection and blur of the image surface and enhance the texture details. Through the research of coal gangue images with different particle sizes, it is found that the image quality and feature curve distribution of small particle size are different from those of large particle size, and the gradient features are worse. In this paper, the accurate identification of particle size is realized using the difference in image quality and texture, and the identification rate is 99.25%. Through the image enhancement method in this paper, 33.41% of the reflection on the image surface is removed, and the average gradient is improved by 74.01%, which effectively improves the image quality and the ability to express texture information. This algorithm has high environmental adaptability, and the identification rate can reach 99.16% in moderate illumination, 98.33% in dim illumination, and 96.33% in strong illumination. This research provides a valuable idea for image processing and identification technology based on machine vision.