本文選題：煤　切入點：矸石　出處：《煤炭科學技術》2017年07期 　論文類型：期刊論文

【摘要】：為實現(xiàn)煤矸自動識別及快速分選,利用煤與矸石的紋理特征與輪廓表面灰度級別等差異較大的特點,對于二者的單一顏色灰度圖像,采取平滑濾波、降噪等方法處理,應用差分盒維法求得二者灰度圖像分形維數(shù),以區(qū)分煤與矸石圖像表面紋理粗糙度。試驗結果表明,煤和矸石的分形維數(shù)有明顯的不同,煤的分形維數(shù)要大于矸石,以數(shù)值2.450 0為分界點,煤的分形維數(shù)在分界點之上,矸石的分形維數(shù)在分界點之下。根據(jù)這一差異可有望實現(xiàn)煤矸的自動識別,為實現(xiàn)一種全新的煤矸分選系統(tǒng)奠定理論基礎。
[Abstract]:In order to realize the automatic recognition and fast sorting of coal and gangue, the texture features of coal and gangue and the grayscale level of contour surface are quite different. For the single color grayscale image of coal and gangue, the methods of smoothing filtering and noise reduction are adopted. In order to distinguish the surface roughness of coal and gangue images, the fractal dimension of grayscale image is obtained by differential box dimension method. The experimental results show that the fractal dimension of coal and gangue is obviously different, and the fractal dimension of coal is larger than that of gangue. The fractal dimension of coal is above the boundary point, and the fractal dimension of gangue is below the boundary point. According to this difference, it is possible to realize the automatic recognition of coal and gangue, which will lay a theoretical foundation for the realization of a new separation system of coal and gangue.
【作者單位】： 中國礦業(yè)大學(北京)機電與信息工程學院;
【分類號】：TD94

【參考文獻】

相關期刊論文 前10條

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本文編號：1598698