大输液异物检测图像快速位移补偿方法
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摘要
智能灯检机在进行大输液药液检测时,由于图像位移偏差带来的干扰,利用帧间差分法提取药液中的异物时经常出现误判的现象。针对上述问题,提出了一种基于加速分割测试特征(FAST)的二进制描述符分块匹配算法。首先,通过加速分割测试在不同尺度的图像上检测特征点,并利用非极大值抑制与熵值差法选择出优秀的特征点;然后,利用改进的模板在特征点周围进行采样,形成对尺度变化、噪声干扰及光照变化均有较强鲁棒性的新型二进制描述子,再将描述子进行降维;最后,利用分块匹配策略和阈值法,快速精确地匹配两帧图像,求解出并补偿位移偏差。实验仿真结果表明:在处理192万高像素图像时,该算法整体实时性能上可达到190 ms,其中新型描述子生成仅占96 ms;并且匹配准确率达到99%以上,成功地抑制了空间位置偏移较大的误匹配;计算出的误差远小于现在匹配精度较好的尺度不变特征转换(SIFT)、带方向的二进制鲁棒独立单元特征(ORB)算法,位移补偿量能精确至亚像素级,能快速补偿药瓶在图像中的位移偏差。
When the intelligent inspection machine extracts the impurity in infusion liquid,because of the interference of image displacement deviation,the misjudgment phenomenon always occurs when using the frame difference method to detect the impurity.In order to solve the problem,a new method of binary descriptor block matching was proposed based on Features of Accelerated Segment Test(FAST).Firstly,the feature points were detected by accelerating the segment test on different scales of the image,and the best feature point was chosen by using non-maximal suppression and the entropy difference.Then,the improved template was used for sampling around the feature point,which formed the new binary descriptor with strong robustness to scale changes,noise interference and illumination changes.The dimension of new descriptor was further reduced.Finally,by using the block matching and threshold method,the two frame images were matched quickly and accurately,and the displacement deviation was solved and compensated.The experimental results show that,when processing the 1.92 million pixel image,the overall real-time performance of the proposed method can be up to 190 ms,and the new descriptor generation only accounts 96 ms.The matching accuracy of the proposed algorithm is more than 99%,which suppresses the error matching of large spatial position offset successfully.The calculated deviation error of the proposed method is much less than the existing algorithms of Scale Invariant Feature Transform(SIFT) and ORiented Binary robust independent elementary features(ORB) with high matching precision.And with the displacement compensation which can be accurate to sub-pixel level,the proposed method can rapidly compensate the displacement deviation of the bottle in the image.
When the intelligent inspection machine extracts the impurity in infusion liquid,because of the interference of image displacement deviation,the misjudgment phenomenon always occurs when using the frame difference method to detect the impurity.In order to solve the problem,a new method of binary descriptor block matching was proposed based on Features of Accelerated Segment Test(FAST).Firstly,the feature points were detected by accelerating the segment test on different scales of the image,and the best feature point was chosen by using non-maximal suppression and the entropy difference.Then,the improved template was used for sampling around the feature point,which formed the new binary descriptor with strong robustness to scale changes,noise interference and illumination changes.The dimension of new descriptor was further reduced.Finally,by using the block matching and threshold method,the two frame images were matched quickly and accurately,and the displacement deviation was solved and compensated.The experimental results show that,when processing the 1.92 million pixel image,the overall real-time performance of the proposed method can be up to 190 ms,and the new descriptor generation only accounts 96 ms.The matching accuracy of the proposed algorithm is more than 99%,which suppresses the error matching of large spatial position offset successfully.The calculated deviation error of the proposed method is much less than the existing algorithms of Scale Invariant Feature Transform(SIFT) and ORiented Binary robust independent elementary features(ORB) with high matching precision.And with the displacement compensation which can be accurate to sub-pixel level,the proposed method can rapidly compensate the displacement deviation of the bottle in the image.
引文
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[2]王耀南,陈铁健,贺振东,等.智能制造装备视觉检测控制方法综述[J].控制理论与应用,2015,32(3):273-286.(WANG YN,CHEN T J,HE Z D,et al.Review on the machine vision measurement and control technology for intelligent manufacturing equipment[J].Control Theory and Applications,2015,32(3):273-286.)
[3]LOWE D G.Distinctive image features from scale invariant key points[J].International Journal of Computer Vision,2004,60(2):91-110.
[4]BAY H,ESS A,TUYTELAARS T,et al.Speeded-Up Robust Features(SURF)[J].International Journal on Computer Vision and Image Understanding,2008,110(3):346-359.
[5]颜雪军,赵春霞,袁霞.2DPCA-SIFT:一种有效的局部特征描述方法[J].自动化学报,2014,40(4):675-682.(YAN X J,ZHAO C X,YUAN X.2DPCA-SIFT:An efficient local feature descriptor[J].Acta Automatica Sinica,2014,40(4):675-682.)
[6]SEDAGHAT A,EBADI H.Remote sensing image matching based on adaptive binning SIFT descriptor[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(10):5283-5293.
[7]CALONDER M,LEPETIT V,FUA P.BRIEF:binary robust independent elementary features[C]//Proceedings of the 11th European Conference on Computer Vision:Part IV.Berlin:Springer,2010:778-792.
[8]VANDERGHEYNST P,ORTIZ R,ALAHI A.FREAK:fast retina keypoint[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEEComputer Society,2012:510-517.
[9]RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:an efficient alternative to SIFT or SURF[C]//Proceedings of the 2011 IEEEInternational Conference on Computer Vision.Washington,DC:IEEE Computer Society,2011:2564-2571.
[10]蒲兴成,谭少峰,张毅.基于改进FAST算法的移动机器人导航[J].智能系统学报,2014,9(4):419-424.(PU X C,TAN SF,ZHANG Y.Research on the navigation of mobile robots based on the improved FAST algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(4):419-424.)
[11]孟令江,王挺,姚辰.野外环境下图像中坑区域的提取方法[J].计算机应用,2016,36(4):1132-1136.(MENG L J,WANG T,YAO C.Image segmentation method of pit area in wild environment[J].Journal of Computer Applications,2016,36(4):1132-1136.)
[12]杨作廷,阮萍,翟波.基于图像熵的高动态范围场景的自动曝光算法[J].光子学报,2013,42(6):742-746.(YANG Z T,RUAN P,ZHAI B.Auto-exposure algorithm for scenes with high dynamic range based on image entropy[J].Acta Photonica Sinica,2013,42(6):742-746.)
[13]SMITH S M,BRADY J M.SUSAN:a new approach to low level image processing[J].International Journal of Computer Vision,1997,23(1):45-78.
[14]DENG H,SUN X P,LIU M L,et al.Infrared small-target detection using multiscale gray difference weighted image entropy[J].IEEE Transactions on Aerospace and Electronic Systems,2016,52(1):60-72.
[15]卢鸿波,孙愿,张志敏.采用旋转匹配的二进制局部描述子[J].中国图象图形学报,2013,18(10):1315-1321.(LU H B,SUN Y,ZHANG Z M.Binary local descriptor based on rotative matching[J].Journal of Image and Graphics,2013,18(10):1315-1321.)
[16]张恒,刘大勇,刘艳丽,等.融合深度信息的BRISK改进算法[J].计算机应用,2015,35(8):2285-2290.(ZHANG H,LIUD Y,LIU Y L,et al.Improved binary robust invariant scalable keypoints algorithm fusing depth information[J].Journal of Computer Applications,2015,35(8):2285-2290.)
[17]CHUM O,MATAS J.Matching with PROSAC-Progressive sample consensus[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Patten Recognition.Washington,DC:IEEE Computer Society,2005,2:220-226.
[18]吴成中,王耀南,贺振东,等.基于机器视觉的注射液中不溶异物检测方法研究[J].仪器仪表学报,2015,36(7):1451-1461.(WU C Z,WANG Y N,HE Z D,et al.Research on foreign insoluble particulate detection method for medicinal solution based on machine vision[J].Chinese Journal of Scientific Instrument,2015,36(7):1451-1461.)