图像处理在激光检测模具气泡中的研究
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摘要
为了提高模具的致密性,需要进行模具气泡检测,提出一种基于激光扫描与图像处理的模具气泡检测技术。首先采用三维激光扫描技术进行模具原始图像采集,对采集的模具激光图像通过阈值分割方法进行气泡致密性特征分割,然后采用多尺度小波分解方法对模具气泡特征点进行定位检测,通过分析气泡像素点和正常部位像素点的像素级视觉差异性特征,实现模具气泡准确检测。最后进行模具气泡检测的实验分析,结果表明,采用该方法进行模具气泡检测的精准度较高,误检率较低。
In order to improve the compactness of the mold,it is necessary to detect the mold bubbles.A detectingmethod of the mold bubbles based on laser scanning and image processing is proposed.Firstly,three-dimensional laser scanning technology is used to collect the original image of the mould,and the laser image of the mould is segmented by threshold segmentation method for bubble density characteristics.Then,the multi-scale wavelet decomposition method is used to locate and detect the characteristic points of mould bubbles.The accurate detection of mould bubbles is realized by analyzing the difference of the pixel-level vision between the bubble pixels and the normal part pixels.Finally,the experimental analysis of mould bubble detection is carried out.The results show that the accuracy of the method is high and the false detection rate is low.
In order to improve the compactness of the mold,it is necessary to detect the mold bubbles.A detectingmethod of the mold bubbles based on laser scanning and image processing is proposed.Firstly,three-dimensional laser scanning technology is used to collect the original image of the mould,and the laser image of the mould is segmented by threshold segmentation method for bubble density characteristics.Then,the multi-scale wavelet decomposition method is used to locate and detect the characteristic points of mould bubbles.The accurate detection of mould bubbles is realized by analyzing the difference of the pixel-level vision between the bubble pixels and the normal part pixels.Finally,the experimental analysis of mould bubble detection is carried out.The results show that the accuracy of the method is high and the false detection rate is low.
引文
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[3] 金艳,杨鸿.基于物体振幅和位相的数字全息焦平面定位研究[J].激光杂志,2016,37(7):27-30.
[4] 江涛,吕家恪,李佳.基于兴趣点匹配的图像检索方法[J].激光杂志,2016,37(6):86-89.
[5] 郑海峰.基于多尺度Retinex的超声图像去噪及增强技术[J].激光杂志,2016,37(2):71-73.
[6] 李旭寒,董安国,封建湖.基于多级引导滤波器的图像区域融合算法[J].激光技术,2016,40(5):756-761.
[7] 吴淑婷,张鲲.改进人工神经网络的激光立体成形表面质量预测[J].激光杂志,2016,37(8):33-36.
[8] 呼有军,周昕,乐建明,等.基于压缩感知的单像素傅立叶全息成像系统[J].激光杂志,2016,37(7):7-10.
[9] 徐赐坚,罗风光,王敬华.光分组交换系统中载波抑制光标签的研究[J].激光技术,2016,40(2):183-188.
[10] 陆兴华.一种结合辐射特征的高效红外目标提取算法[J].激光与红外,2016,46(5):634-638.
[11] 冯辅周,张超省,宋爱斌,等.超声红外热像检测中疲劳裂纹的检出概率模型研究[J].红外与激光工程,2016,45(3):304005-0304005(6).
[12] 温佳,马彩文,水鹏朗.改进自适应LBG矢量量化算法在干涉高光谱图像压缩中的应用[J].光谱学与光谱分析,2011,31(4):1033-1037.
[13] 粘永健,辛勤,万建伟,等.基于多波段预测的高光谱图像分布式无损压缩[J].光学精密工程,2012,20(4):906-912.
[14] 李进,金龙旭,李国宁.适于星上应用的高光谱图像无损压缩算法[J].光谱学与光谱分析,2012,32(8):2264-2269.
[15] CHEN S,WANG G,JIA W.Cluster-group based trusted computing for mobile social networks using implicit social behavioral graph [J].Future Generation Computer Systems,2016,55:391-400.
[16] ZHANG Q Y,WANG R C,SHA C,et al.Node correlation clustering algorithm for wireless multimedia sensor networks based on overlapped FoVs [J].Journal of China Universities of Posts and Telecommunications,2013,20(5):37-44.
[17] Mahmoud E E.Complex complete synchronization of two nonidentical hyperchaotic complex nonlinear systems[J].Mathematical Methods in the Applied Sciences,2014,37(3):321-328.