23 June 1993 Fractal properties from 2D curvature on multiple scales
Author Affiliations +
Abstract
Basic properties of 2-D-nonlinear scale-space representations of images are considered. First, local-energy filters are used to estimate the Hausdorff dimension, DH, of images. A new fractal dimension, DN, defined as a property of 2-D-curvature representations on multiple scales, is introduced as a natural extension of traditional fractal dimensions, and it is shown that the two types of fractal dimensions can give a less ambiguous description of fractal image structure. Since fractal analysis is just one (limited) aspect of scale-space analysis, some more general properties of curvature representations on multiple scales are considered. Simulations are used to analyze the stability of curvature maxima across scale and to illustrate that spurious resolution can be avoided by extracting 2-D-curvature features.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Erhardt Barth, Christoph Zetzsche, Mario Ferraro, Ingo Rentschler, "Fractal properties from 2D curvature on multiple scales", Proc. SPIE 2031, Geometric Methods in Computer Vision II, (23 June 1993); doi: 10.1117/12.146648; https://doi.org/10.1117/12.146648
PROCEEDINGS
13 PAGES


SHARE
KEYWORDS
Fractal analysis

Computer vision technology

Machine vision

Visualization

Neurons

Visual process modeling

Image filtering

Back to Top