An Improved Phase Correlation Method Based on 2-D Plane Fitting and the Maximum Kernel Density Estimator

In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

[1]  Jinchang Ren,et al.  High-Accuracy Sub-Pixel Motion Estimation From Noisy Images in Fourier Domain , 2010, IEEE Transactions on Image Processing.

[2]  Hassan Foroosh,et al.  Extension of phase correlation to subpixel registration , 2002, IEEE Trans. Image Process..

[3]  David Suter,et al.  MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation , 2004, International Journal of Computer Vision.

[4]  Qi Tian,et al.  Algorithms for subpixel registration , 1986 .

[5]  T. Higuchi,et al.  A Subpixel Image Matching Technique Using Phase-Only Correlation , 2006, 2006 International Symposium on Intelligent Signal Processing and Communications.

[6]  Michael T. Orchard,et al.  A fast direct Fourier-based algorithm for subpixel registration of images , 2001, IEEE Trans. Geosci. Remote. Sens..

[7]  William Scott Hoge,et al.  A subspace identification extension to the phase correlation method [MRI application] , 2003, IEEE Transactions on Medical Imaging.

[8]  Yusheng Xu,et al.  A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Jian Guo Liu,et al.  High quality DEM generation from PCIAS , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[10]  Ikram E. Abdou,et al.  Practical approach to the registration of multiple frames of video images , 1998, Electronic Imaging.

[11]  Sébastien Leprince,et al.  Automatic and Precise Orthorectification, Coregistration, and Subpixel Correlation of Satellite Images, Application to Ground Deformation Measurements , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[12]  J. P. Lewis.  Fast Normalized Cross-Correlation , 2010 .

[13]  Hanzi Wang,et al.  Maximum kernel density estimator for robust fitting , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Tania Stathaki,et al.  Subpixel Registration With Gradient Correlation , 2011, IEEE Transactions on Image Processing.

[15]  J. G. Liu,et al.  Robust Phase Correlation Methods for Sub-Pixel Feature Matching , 2006 .

[16]  J. G. Liu,et al.  ENHANCED ROBUST PHASE CORRELATION BASED SUB-PIXEL FEATURE MATCHINIG FOR TARGET MOTION ESTIMATION , 2010 .

[17]  Vasileios Argyriou,et al.  A Study of Sub-pixel Motion Estimation using Phase Correlation , 2006, BMVC.

[18]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[19]  Mario Costantini,et al.  A novel phase unwrapping method based on network programming , 1998, IEEE Trans. Geosci. Remote. Sens..

[20]  Hassan Foroosh,et al.  Subpixel estimation of shifts directly in the Fourier domain , 2006, IEEE Transactions on Image Processing.