Gradient- enhanced waterpixels clustering for coal gangue image segmentation

The estimation of gangue content based on image analysis is an essential part of the intelligent production of top coal caving. Image segmentation is a very important step before the image analysis. As a pre-processing tool of image segmentation, high-efficient superpixel algorithms have been widely used in many real-time vision applications. In order to effectively use the over segmentation problem of noise image caused by traditional watershed transform, a gradient-enhanced waterpixels fast clustering segmentation algorithm is proposed to obtain more accurate and robust segmentation results of coal gangue images in less computing time. Firstly, the edge gradient features of the gangue image are highlighted through multi-scale detail enhancement. Secondly, the superpixel with accurate contour is formed based on the watershed transform of the gradient image reconstructed by multi-scale morphology (MMR). Finally, based on the obtained superpixel image, the final segmentation result is obtained by calculating the pixel statistical histogram of each region in the super-pixel image and clustering the super-pixel image by using fuzzy c-means (FCM) clustering algorithm. Experiments performed on coal gangue images demonstrate that the proposed algorithm obtains accurate and continuous target contour and reaches the requirement of human visual characteristics. According to the evaluation index of superpixel algorithm, for complex application scenes with high real-time requirements, such as the process of top coal caving, the proposed super-pixel clustering segmentation method has better segmentation effect compared with the most advanced image segmentation algorithm.