一种基于Zernike矩的局部特征检测方法

doi:10.11896/jsjkx.181202403

摘要/Abstract

摘要： 针对图像发生几何或质量畸变时局部特征区域提取效果不理想的问题,提出了一种基于Zernike矩的具有旋转不变性与尺度不变性的图像局部特征检测算子。该算法利用Zernike矩构建Hessian矩阵,以基于Zernike矩的Hessian矩阵的行列式与迹确定潜在兴趣点的位置,使用非极大值抑制获得多尺度模板下的最大角点响应,再经二维二次插值运算精确定位兴趣点位置,最后利用主曲率进行边缘响应抑制,利用梯度方向直方图确定兴趣点主方向,由兴趣点4×4邻域的8个方向构建描述算子。实验结果表明,该特征检测方法在视角变换、旋转缩放、图像模糊、图像压缩以及光照变化等图像畸变条件下是有效的,且具有良好的抗噪性能。

关键词: Zernike矩, 尺度不变性, 特征提取, 兴趣点检测, 旋转不变性

Abstract: In order to obtain local feature region with better robustness against image geometric or quality deformation,a novel local feature detector based on Zernike moment with rotation invariance and scale invariance was proposed.For an input image,a Hessian matrix derived by Zernike moments (ZM-Hessian) is used to detect interest points.Firstly,the interest points are located by the difference of the determinate and trace of the ZM-Hessian matrix approximately.Then,the non-maximum-suppression method is applied to capture maximum corner response under multi-scale masks.After that,2D parabolic interpolation is employed to locate the interest points precisely at the sub-pixel level.At last,principal curvature is employed to eliminate edge points.Gradient histogram is employed to obtain dominant orientation.The vector of descriptors are constructed by 4-by-4 neighbors’ 8 directions of interest points.The proposed detector was compared with other traditional detectors based on Mikolajczyk’sframework.Experiment results prove that the proposed method is effective under various image deformations such as angle transformation,rotation & zoom,image blur,image compression,illumination change,and has good anti-noise performance.

Key words: Feature extraction, Interest point detection, Rotational invariance, Scale invariance, Zernike moment

中图分类号:

TP391

贺超雷,毕秀丽,肖斌. 一种基于Zernike矩的局部特征检测方法[J]. 计算机科学, 2020, 47(2): 135-142. https://doi.org/10.11896/jsjkx.181202403

HE Chao-lei,BI Xiu-li,XIAO Bin. Zernike Moment Based Approach for Local Feature Detection[J]. Computer Science, 2020, 47(2): 135-142. https://doi.org/10.11896/jsjkx.181202403

参考文献

[1]HARRIS C J,STEPHENS M.A combined corner and edge detector[C]∥Proceedings of the the Alvey Vision Conference.Manchester:University of Manchester,1988:147-151.
[2]ELDER J H,ZUCKER S W.Local scale control for edge detection and blur estimation [J].IEEE Transactions on PatternAnalysis and Machine Intelligence,1998,20(7):699-716.
[3]MATAS J,CHUM O,URBAN M,et al.Robust wide-baseline stereo from maximally stable extremal regions [J].Image & Vision Computing,2004,22(10):761-767.
[4]MIKOLAJCZYK K,SCHMID C.Scale & Affine Invariant Interest Point Detectors [J].International Journal of Computer Vision,2004,60(1):63-86.
[5]BAY H,ESS A,TUYTELAARS T,et al.Speeded-Up Robust Features [J].Computer Vision & Image Understanding,2008,110(3):404-417.
[6]Lindeberg T.Feature Detection with Automatic Scale Selection[J].International Journal of Computer Vision,1998,30(2):79-116.
[7]MIKOLAJCZYK K,SCHMID C.An Affine Invariant Interest Point Detector[C]∥Proceedings of the European Conference on Computer Vision.Heidelberg:Springer,2002:128-142.
[8]SHI F,HUANG X,DUAN Y.Robust Harris-Laplace Detector by Scale Multiplication[C]∥Proceedings of the International Symposium on Visual Computing.New York:Springer,2009:265-274.
[9]LOWE D G.Distinctive Image Features from Scale-Invariant Key-points [J].International Journal of Computer Vision,2004,60(2):91-110.
[10]KE Y,SUKTHANKAR R.PCA-SIFT:a more distinctive representation for local image descriptors[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2004:506-513.
[11]ABDELHAKIM A E,FARAG A A.CSIFT:A SIFT Descriptor with Color Invariant Characteristics[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2006:1978-1983.
[12]BALNTAS V,TANG L,MIKOLAJCZYK K.Binary Online Learned Descriptors [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(3):555-567.
[13]VERDIE Y,YI K M,FUA P,et al.TILDE:A temporally inva-riant learned detector[C]∥Proceedings of the the IEEE Con-ference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2015:5279-5288.
[14]ZENG A,SONG S,NIEβNER M,et al.3dmatch:Learning local geometric descriptors from rgb-d reconstructions[C]∥Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2017:199-208.
[15]MARKUŠ N,PANDŽIC I,AHLBERG J.Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion:Applications to Face Matching,Learning From Unlabeled Vi-deos and 3D-Shape Retrieval [J].IEEE Transactions on Image Processing,2019,28(1):279-290.
[16]LUO Z,SHEN T,ZHOU L,et al.Geodesc:Learning local descriptors by integrating geometry constraints[C]∥Proceedings of the European Conference on Computer Vision.Cham:Sprin-ger,2018:168-183.
[17]HUANG H,KALOGERAKIS E,CHAUDHURI S,et al. Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks [J].ACM Transactions on Graphics,2018,37(1):6.
[18]MUKUNDAN R,ONG S H,LEE P A.Image analysis by Tchebichef moments [J].IEEE Transactions on Image Processing,2001,10(9):1357-1364.
[19]YAP P T,PARAMESRAN R,ONG S H.Image analysis by Krawtchouk moments [J].IEEE Transactions on Image Processing,2003,12(11):1367-1377.
[20]ZHU H Z,SHU H,LIANG J,et al.Image analysis by discrete orthogonal Racah moments [J].Signal Processing,2007,87(4):687-708.
[21]YAP P T,PARAMESRAN R,ONG S H.Image Analysis Using Hahn Moments [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(11):2057-2062.
[22]DJUROVIC I,STANKOVIC S,PITAS I.Digital watermarking in the fractional Fourier transformation domain [J].Journal of Network and Computer Applications,2001,24(2):167-173.
[23]KARAKASIS E G,PAPAKOSTAS G A,KOULOURIOTIS D E,et al.A Unified Methodology for Computing Accurate Quaternion Color Moments and Moment Invariants [J].IEEE Transactions on Image Processing,2014,23(2):596-611.
[24]CHEN B J,SHU H Z,CHEN G,et al.Color face recognition based on quaternion Zernike moment invariants and quaternion BP neural network [J].Energy Procedia,2011,446(13):551-558.
[25]BIN T,LEI A,JIWEN C,et al.Subpixel edge location based on orthogonal Fourier-Mellin moments [J].Image and Vision Computing,2008,26(4):563-569.
[26]YANG Z,COHEN F S.Cross-weighted moments and affine invariants for image registration and matching [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(8):804-814.
[27]SU L,LI C,LAI Y,et al.A Fast Forgery Detection Algorithm Based on Exponential-Fourier Moments for Video Region Duplication [J].IEEE Transactions on Multimedia,2018,20(4):825-840.
[28]TAN C W,KUMAR A.Accurate iris recognition at a distance using stabilized iris encoding and Zernike moments phase features [J].IEEE Transactions on Image Processing,2014,23(9):3962-3974.
[29]KHOTANZAD A,HONG Y H.Invariant image recognition by Zernike moments [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(5):489-497.
[30]XIN Y,LIAO S X,PAWLAK M.On the improvement of rotational invariance of Zernike moments[C]∥Proceedings of the International Conference on Image Processing.Piscataway:IEEE,2005:842-845.
[31]ZHU H,LIU M,JI H,et al.Combined invariants to blur and rotation using Zernike moment descriptors [J].Pattern Analysis and Applications,2010,13(3):309-319.
[32] ZBULAK G,GÖKMEN M.A rotation invariant local Zernike moment based interest point detector[C]∥Proceedings of the Seventh International Conference on Machine Vision.Bellingham:SPIE,2015:94450E.
[33]NEUBECK A,VAN GOOL L.Efficient Non-Maximum Sup-pression[C]∥Proceedings of the International Conference on Pattern Recognition.Los Alamitos:IEEE,2006:850-855.
[34]MIKOLAJCZYK K,TUYTELAARS T,SCHMID C,et al.A Comparison of Affine Region Detectors [J].International Journal of Computer Vision,2005,65(1／2):43-72.
[35]TUYTELAARS T,VAN GOOL L.Content-Based Image Re-trieval Based on Local Affinely Invariant Regions[C]∥Procee-dings of the International Conference on Advances in Visual In-formation Systems.Berlin:Springer,1999:493-500.
[36]TUYTELAARS T,VAN GOOL L.Wide Baseline Stereo Matching based on Local,Affinely Invariant Regions[C]∥Procee-dings of the British Machine Vision Conference.Amsterdam:Elsevier,2000:1-14.
[37]LEUTENEGGER S,CHLI M,SIEGWART R.BRISK:Binary Robust invariant scalable keypoints[C]∥Proceedings of the In-ternational Conference on Computer Vision.Piscataway:IEEE,2011:2548-2555.
[38]ROSTEN E,PORTER R,DRUMMOND T.Faster and Better:A Machine Learning Approach to Corner Detection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(1):105-119.
[39]CHEN Z,SUN S K.A Zernike moment phase-based descriptor for local image representation and matching [J].IEEE Transactions on Image Processing 2010,19(1):205-219.
[40]PADILLAVIVANCO A,MARTINEZRAMIREZ A,GRANA-DOSAGUSTIN F.Digital image reconstruction using Zernike moments [C]∥Proceedings of SPIE－The Internatioanl Society for Optical Engineering.2004.
[41]TRIPATHY J.Reconstruction of Oriya Alphabets Using Zernike Moments [J].International Journal of Computer Applications,2010,8(8):24-28.

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