Comparison of feature-list cross-correlation algorithms with common cross-correlation algorithms

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.