The Community for Technology Leaders
RSS Icon
Issue No.10 - October (2010 vol.32)
pp: 1899-1906
Georgios Tzimiropoulos , Imperial College, London, UK
Vasileios Argyriou , Kingston University London, Surrey
Stefanos Zafeiriou , Imperial College, London, UK
Tania Stathaki , Imperial College, London, UK
We present a robust FFT-based approach to scale-invariant image registration. Our method relies on FFT-based correlation twice: once in the log-polar Fourier domain to estimate the scaling and rotation and once in the spatial domain to recover the residual translation. Previous methods based on the same principles are not robust. To equip our scheme with robustness and accuracy, we introduce modifications which tailor the method to the nature of images. First, we derive efficient log-polar Fourier representations by replacing image functions with complex gray-level edge maps. We show that this representation both captures the structure of salient image features and circumvents problems related to the low-pass nature of images, interpolation errors, border effects, and aliasing. Second, to recover the unknown parameters, we introduce the normalized gradient correlation. We show that, using image gradients to perform correlation, the errors induced by outliers are mapped to a uniform distribution for which our normalized gradient correlation features robust performance. Exhaustive experimentation with real images showed that, unlike any other Fourier-based correlation techniques, the proposed method was able to estimate translations, arbitrary rotations, and scale factors up to 6.
Global motion estimation, correlation methods, FFT, scale-invariant image registration, frontal view face registration.
Georgios Tzimiropoulos, Vasileios Argyriou, Stefanos Zafeiriou, Tania Stathaki, "Robust FFT-Based Scale-Invariant Image Registration with Image Gradients", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 10, pp. 1899-1906, October 2010, doi:10.1109/TPAMI.2010.107
[1] B.S. Reddy and B.N. Chatterji, "An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration," IEEE Trans. Image Processing, vol. 5, no. 8, pp. 1266-1271, Aug. 1996.
[2] C.D. Kuglin and D.C. Hines, "The Phase Correlation Image Alignment Method," Proc. IEEE Conf. Cybernetics and Soc., pp. 163-165, 1975.
[3] Y. Keller, A. Averbuch, and M. Israeli, "Pseudopolar-Based Estimation of Large Translations, Rotations and Scalings in Images," IEEE Trans. Image Processing, vol. 14, no. 1, pp. 12-22, Jan. 2005.
[4] H. Liu, B. Guo, and Z. Feng, "Pseudo-Log-Polar Fourier Transform for Image Registration," IEEE Signal Processing Letters, vol. 13, no. 1, pp. 17-21, Jan. 2006.
[5] W. Pan, K. Qin, and Y. Chen, "An Adaptable-Multilayer Fractional Fourier Transform Approach for Image Registration," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 400-413, Mar. 2009.
[6] A. Averbuch, D.L. Donoho, R.R. Coifman, and M. Israeli, "Fast Slant Stack: A Notion of Radon Transform for Data in Cartesian Grid Which Is Rapidly Computable, Algebrically Exact, Geometrically Faithful and Invertible," SIAM J. Scientific Computing, to appear.
[7] S. Zokai and G. Wolberg, "Image Registration Using Log-Polar Mappings for Recovery of Large-Scale Similarity and Projective Transformations," IEEE Trans. Image Processing, vol. 14, no. 10, pp. 1422-1434, Oct. 2005.
[8] A.J. Fitch, A. Kadyrov, W.J. Christmas, and J. Kittler, "Orientation Correlation," Proc. British Machine Vision Conf., pp. 133-142, 2002.
[9] V. Argyriou and T. Vlachos, "Estimation of Sub-Pixel Motion Using Gradient Cross-Correlation," Electronic Letters, vol. 39, no. 13, pp. 980-982, 2003.
[10] R.C. Gonzalez and R.E. Woods, Digital Image Processing, second ed. Pearson Education, 2002.
[11] R.N. Bracewell, K.-Y. Chang, A.K. Jha, and Y.-H. Wang, "Affine Theorem for Two-Dimensional Fourier Transform," Electronics Letters, vol. 29, no. 3, pp. 304-309, 1993.
[12] Y. Keller and A. Averbuch, "A Projection-Based Extension to Phase Correlation Image Alignment," Signal Processing, vol. 87, pp. 124-133, 2007.
[13] A.L. Garcia, Probability and Random Processes for Electrical Engineering, second ed. Pearson Education, 2004.
[14] F.J. Harris, "On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform," Proc. IEEE, vol. 66, no. 1, pp. 51-83, Jan. 1978.
[15] H.S. Stone, B. Tao, and M. MacGuire, "Analysis of Image Registration Noise Due to Rotationally Dependent Aliasing," J. Visual Comm. and Image Representation, vol. R.14, pp. 114-135, 2003.
[16] Image Database, /, 2010.
[17] A. Averbuch, R.R. Coifman, D.L. Donoho, M. Elad, and M. Israeli, "Fast and Accurate Polar Fourier Transform," Applied and Computational Harmonic Analysis, vol. 21, pp. 145-167, 2006.
[18] Image Database, /, 2010.
[19] P.J. Phillips, H. Wechsler, J. Huang, and P. Rauss, "The Feret Database and Evaluation Procedure for Face Recognition Algorithms," Image and Vision Computing J., vol. 16, no. 5, pp. 295-306, 1998.
[20] P.J. Phillips, H. Moon, P.J. Rauss, and S. Rizvi, "The Feret Evaluation Methodology for Face Recognition Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, Oct. 2000.
[21] D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[22] C.V. Stewart, C.-L. Tsai, and B. Roysam, "The Dual-Bootstrap Iterative Closest Point Algorithm with Application to Retinal Image Registration," IEEE Trans. Medical Imaging, vol. 22, no. 11, pp. 1379-1394, Nov. 2003.
[23] Intel Math Kernel Library, , 2010.
[24] M. Frigo and S.G. Johnson, "FFTW on the Cell Processor,", 2007.
[25] N.K. Govindaraju, B. Lloyd, Y. Dotsenko, B. Smith, and J. Manferdelli, "High Performance Discrete Fourier Transforms on Graphics Processors," Proc. ACM/IEEE Conf. Supercomputing, 2008.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool