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Local fractal geometric features for image segmentation
Author(s) -
Keller James M.,
Seo YoungBo
Publication year - 1990
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.1850020404
Subject(s) - fractional brownian motion , fractal dimension , artificial intelligence , segmentation , fractal , scale invariance , computer vision , pattern recognition (psychology) , invariant (physics) , fractal analysis , feature (linguistics) , mathematics , image segmentation , computer science , brownian motion , mathematical analysis , statistics , linguistics , philosophy , mathematical physics
In this article, features based on fractal geometry are used for segmentation of synthetic and natural scenes. Assuming a fractional Brownian motion model of image regions, we extract, at each pixel, small, one‐variable “slices” in each of four directions from which we estimate two features: the fractal dimension and the intercept. While the fractal dimension has received most attention recently as a scale‐invariant feature, we show that the intercept is related to the dimension and possesses even better discriminatory power for segmentation purposes when calculations are made on small, one variable windows. These parameters are studied as segmentation features on both composite images of synthetically generated fractional Brownian motion surfaces and on intensity images of natural scenes.