CAJ | 학술논문

　　为了提高铁谱分析自动化程度，结合脉冲耦合神经网络（PCNN）和数学形态学，提出了一种铁谱图像典型磨粒自动提取方法。利用综合色距函数将彩色铁谱图像三通道问题转化为单通道问题，使分割问题简化；利用简化PCNN间接实现铁谱图像分割，并采用数学形态学对获得的二值图像进行处理；运用数学形态学中连通域提取算法自动提取图像中的典型磨粒。实验结果表明：与其他方法相比，该方法能快速有效地分割铁谱磨粒图像，并实现铁谱图像中典型磨粒的自动提取。
　　위료제고철보분석자동화정도，결합맥충우합신경망락（PCNN）화수학형태학，제출료일충철보도상전형마립자동제취방법。이용종합색거함수장채색철보도상삼통도문제전화위단통도문제，사분할문제간화；이용간화PCNN간접실현철보도상분할，병채용수학형태학대획득적이치도상진행처리；운용수학형태학중련통역제취산법자동제취도상중적전형마립。실험결과표명：여기타방법상비，해방법능쾌속유효지분할철보마립도상，병실현철보도상중전형마립적자동제취。
To improve the automation of ferrography, mathematical morphology is combined with Pulse Coupled Neural Network (PCNN), then an automatic extraction method of wear particles for ferrographic images is proposed. This method employs synthe-sis color distance function to transform three-channel issue of color ferrographic images into single-channel issue. Thus, image segmentation is simplified. Then ferrographic images are indirectly segmented by simplified PCNN, and achieved binary images are processed utilizing mathematical morphology. With connected domain extraction algorithm of mathematical morphology, typical wear particles are extracted automatically. Experiments results show that compared with other methods, the proposed method can segment color ferrographic images effectively and realize automatic extraction of typical wear particles.