Comparison of Feature-List Cross-Correlation Algorithms with Common Cross-Correlation Algorithms
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Research Article Comparison of Feature-List Cross-Correlation Algorithms with Common Cross-Correlation Algorithms Ralph Maschotta,1 Simon Boymann,1 and Ulrich Hoppe2 1 Institute
of Biomedical Engineering and Informatics, Ilmenau Technical University, P.O. Box 100565, 98693 Ilmenau, Germany 2 Department of Audiology, University Hospital of Erlangen-Nuremberg, Waldstr. 1, 91054 Erlangen, Germany Received 1 August 2005; Revised 20 December 2006; Accepted 21 December 2006 Recommended by Rafael Molina This paper presents a feature-list cross-correlation algorithm based on: a common feature extraction algorithm, a transformation of the results into a feature-list representation form, and a list-based cross-correlation algorithm. The feature-list cross-correlation algorithms are compared with known results of the common cross-correlation algorithms. Therefore, simple test images containing different objects under changing image conditions and with several image distortions are used. In addition, a medical application is used to verify the results. The results are analyzed by means of curve progression of coefficients and curve progression of peak signal-to-noise ratio (PSNR). As a result, the presented feature list cross-correlation algorithms are sensitive to all changes of image conditions. Therefore, it is possible to separate objects that are similar but not equal. Because of the high quantity of feature points and the strong PSNR, the loss of a few feature points does not have a significant influence on the detection results. These results are confirmed by a successfully applied medical application. The calculation time of the feature list cross-correlation algorithms only depends on the length of the feature-lists. The amount of feature points is much less than the number of pixels in the image. Therefore, the feature-list cross-correlation algorithms are faster than common cross-correlation algorithms. Better image conditions tend to reduce the size of the feature-list. Hence, the processing time decreases considerably. Copyright © 2007 Ralph Maschotta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
The two-dimensional cross-correlation is a simple and robust algorithm used to solve different problems in the field of image processing. However, when images are rotated, scaled, or include other image distortions the computation time increases considerably. Furthermore, extensive changes in brightness, contrast, or strong outliers cause false results [1]. Because of this, optimized algorithms have been developed, namely, the cross-correlation algorithm based on least squared error [2], the block-matching algorithms [3], techniques based on the Fourier transform [4], waveletbased techniques [5], and feature-based techniques [2, 6–10]. These algorithms are used for many problems such as motion estimation [11], video coding [12], targ
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