Pseudo-correlation: a fast, robust, absolute, gray-level image alignment algorithm

Thomas J. Radcliffe; Rasika Rajapakshe; Shlomo Shalev

doi:10.1117/12.154498

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14 September 1993 Pseudo-correlation: a fast, robust, absolute, gray-level image alignment algorithm

Thomas J. Radcliffe, Rasika Rajapakshe, Shlomo Shalev

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Proceedings Volume 1898, Medical Imaging 1993: Image Processing; (1993) https://doi.org/10.1117/12.154498
Event: Medical Imaging 1993, 1993, Newport Beach, CA, United States

Abstract

A new image alignment algorithm--pseudo-correlation--has been developed based on the application of Monte Carlo techniques to the calculation of a cross-correlation integral for grey-scale images. It has many advantages over cross-correlation: it is at least a factor of ten faster than FFT-based cross-correlation, and requires eight times less memory. Its high speed allows for the search space of geometric transformations between images to include magnification and rotation as well as translations without the search time becoming too long. It allows noise to be taken into account, making calculation of a robust, absolute probability of good alignment possible. It is relatively insensitive to differences in quality between images. This paper describes the pseudo-correlation algorithm and presents the results of tests of the effects of contrast enhancement and noise on the algorithm's performance. These tests show that the algorithm is well-suited to the task of automated alignment of very low contrast images from video electronic portal imaging devices (VEPIDs).

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Thomas J. Radcliffe, Rasika Rajapakshe, and Shlomo Shalev "Pseudo-correlation: a fast, robust, absolute, gray-level image alignment algorithm", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); https://doi.org/10.1117/12.154498

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