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Surrogate data: A novel approach to object detection

100%
Tabor Z.
International Journal of Applied Mathematics and Computer Science
|
2010
|
tom 20
|
nr 3
545-553
EN
In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax intensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.
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Statistical estimation of the dynamics of watershed dams

100%
Tabor Z.
International Journal of Applied Mathematics and Computer Science
|
2009
|
tom 19
|
nr 2
349-360
EN
In the present study the notion of watershed contour dynamics, defined within the framework of mathematical morphology, is examined. It is shown that the dynamics are a direct measure of the “sharpness” of transition between neighboring watershed basins. The expressions for the expected value and the statistical error of the estimation of contour dynamics are derived in the presence of noise, based on extreme value theory. The sensitivity of contour dynamics to noise is studied. A statistical approach to the notion of contour dynamics is developed and a definition of statistical dynamics is proposed.
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