A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Hough transforms for curve detection
Computer Vision and Image Understanding
Optical character recognition
Machine Vision
Scene-consistent detection of feature points in video sequences
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Motion vector estimation using line-square search block matching algorithm for video sequences
EURASIP Journal on Applied Signal Processing
Algorithms for hardware-based pattern recognition
EURASIP Journal on Applied Signal Processing
Robust face imagematching under illumination variations
EURASIP Journal on Applied Signal Processing
Fast curve estimation using preconditioned generalized Radon transform
IEEE Transactions on Image Processing
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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.