针对在埋弧焊X射线焊缝图像的强噪声和弱对比度特点下常规图像分割算法成功率低的现状,通过综合的分析和实验有针对性地给出了系统、实用的缺陷分割方法。首先通过实验给出了一种提取焊缝图像感兴趣区域(the region of interest,ROI)的...针对在埋弧焊X射线焊缝图像的强噪声和弱对比度特点下常规图像分割算法成功率低的现状,通过综合的分析和实验有针对性地给出了系统、实用的缺陷分割方法。首先通过实验给出了一种提取焊缝图像感兴趣区域(the region of interest,ROI)的方法。该方法通过中值滤波,基于sin函数的图像增强、大津法分割、Sobel算子边缘检测和Hough变换可以定量计算出X射线焊缝图像的ROI区域。进一步通过实验给出基于大津法的焊缝缺陷分割算法,实验表明在无人工设定初始分割阈值情况下这一分割算法具有较高的分割成功率。为进一步提高分割成功率,针对焊缝缺陷相对面积较小的特点,提出将缺陷视为噪声,将平均局部平均灰度视为密度,利用密度聚类方法进行缺陷分割。该方法在78张有缺陷的焊缝图像中,成功地分割出74张图像中的缺陷。最后在所研究算法基础上给出了一个综合考虑到各种因素的分割算法,即可节约计算时间,又可以保证分割的成功率。展开更多
Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect i...Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect images, we used convex optimization(CO) with different weights as a pretreatment method for smoothing and the Otsu segmentation method to obtain the target defect area images. Structural similarity(SSIM) results between original image and defect image were calculated to evaluate the performance of segmentation with different convex optimization weights. The geometric and intensity features of defects were extracted before constructing a classification and regression tree(CART) classifier. The average accuracy of the classifier is 94.1% with four types of defects on Xylosma congestum wood plate surface: pinhole, crack,live knot and dead knot. Experimental results showed that CO can save the edge of target defects maximally, SSIM can select the appropriate weight for CO, and the CART classifier appears to have the advantages of good adaptability and high classification accuracy.展开更多
In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems...In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6%for wood defect images with a complex background.展开更多
文摘针对在埋弧焊X射线焊缝图像的强噪声和弱对比度特点下常规图像分割算法成功率低的现状,通过综合的分析和实验有针对性地给出了系统、实用的缺陷分割方法。首先通过实验给出了一种提取焊缝图像感兴趣区域(the region of interest,ROI)的方法。该方法通过中值滤波,基于sin函数的图像增强、大津法分割、Sobel算子边缘检测和Hough变换可以定量计算出X射线焊缝图像的ROI区域。进一步通过实验给出基于大津法的焊缝缺陷分割算法,实验表明在无人工设定初始分割阈值情况下这一分割算法具有较高的分割成功率。为进一步提高分割成功率,针对焊缝缺陷相对面积较小的特点,提出将缺陷视为噪声,将平均局部平均灰度视为密度,利用密度聚类方法进行缺陷分割。该方法在78张有缺陷的焊缝图像中,成功地分割出74张图像中的缺陷。最后在所研究算法基础上给出了一个综合考虑到各种因素的分割算法,即可节约计算时间,又可以保证分割的成功率。
基金supported by the Fund of Forestry 948project(2015-4-52)the Fundamental Research Funds for the Central Universities(2572017DB05)the Natural Science Foundation of Heilongjiang Province(C2017005)
文摘Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect images, we used convex optimization(CO) with different weights as a pretreatment method for smoothing and the Otsu segmentation method to obtain the target defect area images. Structural similarity(SSIM) results between original image and defect image were calculated to evaluate the performance of segmentation with different convex optimization weights. The geometric and intensity features of defects were extracted before constructing a classification and regression tree(CART) classifier. The average accuracy of the classifier is 94.1% with four types of defects on Xylosma congestum wood plate surface: pinhole, crack,live knot and dead knot. Experimental results showed that CO can save the edge of target defects maximally, SSIM can select the appropriate weight for CO, and the CART classifier appears to have the advantages of good adaptability and high classification accuracy.
基金supported by National Forestry Public Welfare Industry Scientific Research Special Subsidy Project(201304502)
文摘In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6%for wood defect images with a complex background.
