Subpixel registration with gradient correlation

Tzimiropoulos, Georgios, Argyriou, V. and Stathaki, T. (2011) Subpixel registration with gradient correlation. IEEE Transactions on Image Processing, 20 (6). pp. 1761-1767. ISSN 1057-7149

Full content URL:

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive


We address the problem of subpixel registration of images assumed to be related by a pure translation. We present a method which extends gradient correlation to achieve subpixel accuracy. Our scheme is based on modeling the dominant singular vectors of the 2-D gradient correlation matrix with a generic kernel which we derive by studying the structure of gradient correlation assuming natural image statistics. Our kernel has a parametric form which offers flexibility in modeling the functions obtained from various types of image data. We estimate the kernel parameters, including the unknown subpixel shifts, using the Levenberg-Marquardt algorithm. Experiments with LANDSAT and MRI data show that our scheme outperforms recently proposed state-of-the-art phase correlation methods. © 2010 IEEE.

Keywords:Gradient correlation, Image data, Kernel parameter, LANDSAT, Levenberg-Marquardt algorithm, Natural image statistics, Parametric forms, Phase correlation method, Singular vectors, Sub pixels, Subpixel accuracy, Subpixel registration, Subpixel shift, Image registration, Correlation methods, algorithm, automated pattern recognition, computer assisted diagnosis, image enhancement, image subtraction, letter, methodology, reproducibility, sensitivity and specificity, signal processing, Algorithms, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Reproducibility of Results, Signal Processing, Computer-Assisted, Subtraction Technique
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:8733
Deposited On:04 Apr 2013 20:37

Repository Staff Only: item control page