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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Manipulation and presentation of multidimensional image data using the peano scan.

R J Stevens¹, A F Lehar, F H Preston

¹Scientific Research and Development Branch, Home Office, Woodcock Hill Sandridge, St. Albans, Hertford-shire AL4 9HQ, England.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

Giuseppe Peano

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Area of Science:

Image Processing
Fractal Geometry
Computer Vision

Background:

Fractal curves, like the one introduced by Giuseppe Peano, possess statistical properties beneficial for image processing.
These curves can map one-dimensional lines to n-dimensional spaces, preserving spatial relationships.

Purpose of the Study:

To explore the application of Peano fractal curves in image processing tasks.
To demonstrate their utility in color image display, compression, and classification.

Main Methods:

Utilizing Peano fractal curves as a transform for image data.
Implementing a simple algorithm for constructing these fractal curves.
Integrating the fractal curve method with existing spatial compression techniques.

Main Results:

Enabled high-quality color image display on systems with limited color palettes (256 colors).
Facilitated the selection of logically ordered color sets for false color applications.
Achieved approximately a 3X improvement in color image compression when combined with spatial compression.

Conclusions:

Peano fractal curves offer a versatile tool for enhancing color image display and compression.
The technique provides a method for efficient representation and manipulation of image data in n-dimensional space.
This approach offers practical advantages for digital imaging systems with display or storage constraints.