A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of digital image processing
Fundamentals of digital image processing
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Contour tracking by enhancing corners and junctions
Computer Vision and Image Understanding
A DSP-Based Real Time Contour Tracking System
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Image geometry through multiscale statistics
Image geometry through multiscale statistics
Contour Tracking When Two Gray-Level Discontinuities Are Close to Each Other
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
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The gray-level moments are usually used in image processing literature to describe how the gray-levels of a finite domain of the image are distributed with respect to the mean level. However, the gray-level central and absolute central moments can provide zero-crossings and ridges, respectively, at gray-level discontinuities as well as conventional operators like the Laplacian of Gaussian and the gradient of Gaussian. A mass center b of the gray-level variability can be also defined. When given a starting point p, vector b indicates the path which joins p to the nearest gray-level discontinuity. Moreover, when a moment of even order is used, vector b can indicate a point which is closer to the discontinuity than p regardless of the distance between p and the discontinuity. Therefore, given an approximate starting contour, a discontinuity can be located by iteratively computing the mass centers of the points of the starting contour.