ABSTRACT

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.

摘要

基于图像分析的煤矸石含量估算是放顶煤智能化生产的重要组成部分。图像分割是图像分析之前的一个非常重要的步骤。高效的超像素算法作为图像分割的预处理工具，已经广泛应用于许多实时视觉应用中。为有效利用传统分水岭变换引起的噪声图像过分割问题，提出一种梯度增强水像素快速聚类分割算法，以较少的计算时间获得更准确、更稳健的煤矸石图像分割结果。首先，通过多尺度细节增强，突出脉石图像的边缘梯度特征。第二，基于多尺度形态学（MMR）重建的梯度图像的分水岭变换，形成具有精确轮廓的超像素。最后，在得到的超像素图像的基础上，计算超像素图像中各区域的像素统计直方图，采用模糊c均值(FCM)聚类算法对超像素图像进行聚类，得到最终的分割结果。在煤矸石图像上进行的实验表明，该算法得到的目标轮廓准确、连续，达到了人眼视觉特性的要求。根据超像素算法的评价指标，对于实时性要求高的复杂应用场景，如放顶煤过程，