Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Comparison of Feature-List Cross-Correlation Algorithms with Common Cross-Correlation Algorithms | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 89150 15 pages doi 2007 89150 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 . 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 verily 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 .
