New method for subpixel image matching with rotation invariance by combining the parametric template method and the ring projection transform process

The need for accurate and efficient computation of template matching prevails in many applications. However, a challenge in tem- plate matching is to obtain high accuracy that involves acceptable com- putation complexity and is robust to rotation. A new subpixel template matching approach that combines the parametric template method and the ring projection transform process is proposed. It not only achieves subpixel accuracy in location, but also offers rotation invariance in the subpixel template matching. Furthermore, our approach is conceptually simple, easy to implement, and very efficient because no iterative steps are involved. The simulated results show that our approach enjoys very high precision in the presence of image rotations. Experiments with real- world scenes demonstrate that the proposed method can reach subpixel accuracy for finding the distance between two target objects in the pres- ence of rotations and translations. This indicates that our approach is suitable for accurate on-line template matching with scene rotations and translations. © 2006 Society of Photo-Optical Instrumentation Engineers.

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

[2]  A. Ardeshir Goshtasby,et al.  A Two-Stage Cross Correlation Approach to Template Matching , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jianming Wu,et al.  Algorithm of subpixel image matching with high accuracy , 2001, International Symposium on Multispectral Image Processing and Pattern Recognition.

[4]  Hussein Baher,et al.  Analog & digital signal processing , 1990 .

[5]  Augusto Sarti,et al.  Improving the performance of edge localization techniques through error compensation , 1998, Signal Process. Image Commun..

[6]  Y. Tang,et al.  Ring-projection-wavelet-fractal signatures: a novel approach to feature extraction , 1998 .

[7]  William M. Silver Normalized Correlation Search In Alignment, Gauging, And Inspection , 1987, Photonics West - Lasers and Applications in Science and Engineering.

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

[9]  H. Yasuda Standardization activities on multimedia coding in ISO , 1989 .

[10]  Shaun S. Gleason,et al.  Subpixel measurement of image features based on paraboloid surface fit , 1991, Other Conferences.

[11]  Jacqueline Le Moigne,et al.  Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient , 2003, IEEE Trans. Image Process..

[12]  Mutsuo Sano,et al.  A parametric template method and its application to robust matching , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Michael Unser,et al.  B-spline signal processing. I. Theory , 1993, IEEE Trans. Signal Process..

[14]  Yuan Yan Tang,et al.  Transformation-Ring-Projection (Trp) Algorithm and its VLSI Implementation , 1991, Int. J. Pattern Recognit. Artif. Intell..

[15]  Min-Seok Choi,et al.  A novel two stage template matching method for rotation and illumination invariance , 2002, Pattern Recognit..

[16]  Michael Unser,et al.  A pyramid approach to subpixel registration based on intensity , 1998, IEEE Trans. Image Process..

[17]  Du-Ming Tsai,et al.  Rotation-invariant pattern matching using wavelet decomposition , 2002, Pattern Recognit. Lett..