图像边缘提取在工件内部缺陷识别中的应用研究（英文）

Application research on im age edge extraction in working-piece innerfaultiness identification

工件内部缺陷是难于检验的,借助图像识别技术可以精确地确定工件内部是否存在缺陷以及界定工件内部缺陷的区域范围等,因此,断层扫描图像分割技术已广泛应用于工件内部的缺陷识别检测。为了克服采用传统分水岭算法分割图象导致的过分割现象,提出了一种图像边缘提取的智能融合算法。首先借助基于模糊形态学的开闭算法对图像做了平滑处理,其次,基于数学形态学计算了梯度算子,最后对梯度图像进行分割获得了期望的图像。仿真对比实验研究验证了该算法可较好地消除过分割现象,在工件内部缺陷图像识别中有更好的实时性与可用性。研究结果表明:提出的智能融合算法对提高图像处理质量有重要参考意义。

The internal defects of the work-piece is difficult to test. Since the image recognition technology can accurately determine the work-piece internal defects and define the internal defects of the work-piece areas,the tomography image segmentation technology has been widely used in the internal defect detection of work-pieces.In order to overcome over-segmentation phenomenon during adopting conventional watershed algorithm to segment the image,this paper proposed a sort of intelligent fusion algorithm of image edge extraction. It could firstly smooth the image with aid of opening-closing algorithm based fuzzy mathematical morphology. Based on the mathematical morphology,it could secondly compute the gradient operators,and finally,it segmented the gradient image to obtain the expected image. The comparative experiment study of simulation demonstrated that it is better in eliminating over segmentation phenomenon,better in real time and more applicable in image recognition. The research result showed that the proposed intelligent fusion algorithm has great reference significance to improve the image processing quality.