ZNCC-based template matching using bounded partial correlation

This paper describes a class of algorithms enabling efficient and exhaustive matching of a template into an image based on the Zero mean Normalized Cross-Correlation function (ZNCC). The approach consists in checking at each image position two sufficient conditions obtained at a reduced computational cost. This allows to skip rapidly most of the expensive calculations required to evaluate the ZNCC at those image points that cannot improve the best correlation score found so far. The algorithms shown in this paper generalize and extend the concept of Bounded Partial Correlation (BPC), previously devised for a template matching process based on the Normalized Cross-Correlation function (NCC).

[1]  Changming Sun,et al.  Fast optical flow using 3D shortest path techniques , 2002, Image Vis. Comput..

[2]  Azriel Rosenfeld,et al.  Two-Stage Template Matching , 1977, IEEE Transactions on Computers.

[3]  Luigi di Stefano,et al.  Fast template matching using bounded partial correlation , 2003, Machine Vision and Applications.

[4]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Du-Ming Tsai,et al.  Fast normalized cross correlation for defect detection , 2003, Pattern Recognit. Lett..

[6]  M. J. McDonnell,et al.  Box-filtering techniques , 1981 .

[7]  Harvey F. Silverman,et al.  A Class of Algorithms for Fast Digital Image Registration , 1972, IEEE Transactions on Computers.

[8]  Laurent Moll,et al.  Real time correlation-based stereo: algorithm, implementations and applications , 1993 .

[9]  Du-Ming Tsai,et al.  The evaluation of normalized cross correlations for defect detection , 2003, Pattern Recognit. Lett..

[10]  Werner Krattenthaler,et al.  Point correlation: a reduced-cost template matching technique , 1994, Proceedings of 1st International Conference on Image Processing.

[11]  Changming Sun,et al.  Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques , 2002, International Journal of Computer Vision.

[12]  Shmuel Peleg,et al.  Fast panoramic stereo matching using cylindrical maximum surfaces , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Luigi di Stefano,et al.  A sufficient condition based on the Cauchy-Schwarz inequality for efficient template matching , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[14]  Olga Veksler,et al.  Fast variable window for stereo correspondence using integral images , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..