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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Cross Correlation versus Mutual Information for Image Mosaicing

Author 1: Sherin Ghannam
Author 2: A. Lynn Abbott

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2013.041113

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 11, 2013.

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Abstract: This paper reviews the concept of image mosaicing and presents a comparison between two of the most common image mosaicing techniques. The first technique is based on normalized cross correlation (NCC) for registering overlapping 2D images of a 3D scene. The second is based on mutual information (MI). The experimental results demonstrate that the two techniques have a similar performance in most cases but there are some interesting differences. The choice of a distinctive template is critical when working with NCC. On the other hand, when using MI, the registration procedure was able to provide acceptable performance even without distinctive templates. But generally the performance when using MI with large rotation angles was not accurate as with NCC.

Keywords: mosaicing; normalized cross correlation; mutual information

Sherin Ghannam and A. Lynn Abbott, “Cross Correlation versus Mutual Information for Image Mosaicing” International Journal of Advanced Computer Science and Applications(IJACSA), 4(11), 2013. http://dx.doi.org/10.14569/IJACSA.2013.041113

@article{Ghannam2013,
title = {Cross Correlation versus Mutual Information for Image Mosaicing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.041113},
url = {http://dx.doi.org/10.14569/IJACSA.2013.041113},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {11},
author = {Sherin Ghannam and A. Lynn Abbott}
}


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