A texture fused superpixel algorithm for coal mine waste rock image segmentation

Abstract

In coal production, it is necessary to separate waste rock from raw coal. With the development of computer vision technology, the image recognition method with simple equipment and high efficiency has become a research hotspot. Image segmentation is a very important step before the image analysis in image recognition method. Traditional image segmentation takes pixel as processing unit, without considering the influence of space and texture; therefore, it is difficult to segment the coal and waste rock images when the target and the background are with similar color and the existence of weak edges and fuzzy regions. In this paper, a new superpixel segmentation algorithm that combines color, spatial position, and texture in clustering is proposed, which integrates texture information into SLIC algorithm. After test of a great number of coal and waste rock images, the segmentation results of SLIC and SLFTIC algorithms were evaluated that SLFTIC can better fit the edge. According to the evaluation index of superpixel algorithm, undersegmentation error and boundary recall of SLFTIC are superior than SLIC, and compactness score is slightly improved. For the images with similar foreground and background and complex texture, such as mineral images and medical images, this kind of superpixel cutting method has better segmentation effect.