Korbicz, Józef - red. ; Uciński, Dariusz - red.
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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, Volume 20, Number 3 (2010) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Sep 7, 2021
Aug 14, 2018
126
https://zbc.uz.zgora.pl/publication/54967
Edition name | Date |
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Surrogate data: a novel approach to object detection | Sep 7, 2021 |
Tabor, Zbisław Korbicz, Józef - ed.