On the gray-level central and absolute central moments and the mass center of the gray-level variability in low-level image processing

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.

[1]  Andrew P. Witkin,et al.  Uniqueness of the Gaussian Kernel for Scale-Space Filtering , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Marcello Demi,et al.  Contour Tracking by Enhancing Corners and Junctions , 1996, Comput. Vis. Image Underst..

[3]  Marcello Demi,et al.  A DSP-based real time contour tracking system , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[4]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  William K. Pratt,et al.  Digital image processing (2nd ed.) , 1991 .

[6]  J. Koenderink The structure of images , 2004, Biological Cybernetics.

[7]  Alan L. Yuille,et al.  Scaling Theorems for Zero Crossings , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Stephen M. Pizer,et al.  Image geometry through multiscale statistics , 1996 .

[10]  William H. Press,et al.  Numerical recipes , 1990 .

[11]  D. Hubel Eye, brain, and vision , 1988 .

[12]  M. Demi The first absolute central moment as an edge detector , 2001 .

[13]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[14]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[15]  Marcello Demi,et al.  The First Absolute Central Moment in Low-Level Image Processing , 2000, Comput. Vis. Image Underst..

[16]  Vincenzo Gemignani,et al.  A real time contour tracking system to investigate the cross-sectional area changes of the aorta , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[17]  M. Demi,et al.  How the map of mass centers of the first order absolute moment can be used to track contours in image sequences [cardiovascular application] , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[18]  Vincenzo Gemignani,et al.  Real time assessment of the endothelial function , 2001, Computers in Cardiology 2001. Vol.28 (Cat. No.01CH37287).

[19]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.