Computer Science ›› 2020, Vol. 47 ›› Issue (2): 135-142.doi: 10.11896/jsjkx.181202403

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Zernike Moment Based Approach for Local Feature Detection

HE Chao-lei,BI Xiu-li,XIAO Bin   

  1. (Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
  • Received:2018-12-24 Online:2020-02-15 Published:2020-03-18
  • About author:HE Chao-lei,born in 1988,master.His main research interests include image processing and pattern recognition;BI Xiu-li,born in 1982,Ph.D,lecture,Her research interests include image processing,multimedia security and ima-ge forensics.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61572092), NSFC-Guangdong Union Foundation (U1401252) and National Science & Technology Major Project (2016YFC1000307-3).

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

CLC Number: 

  • TP391
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