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Proceedings Paper

Continuous-tone image recognition using fractal theory
Author(s): Ying Liu
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Paper Abstract

In this paper, we study pattern recognition using Probabilistic Iterated Function Systems (PIFS). A learning system can be defined by three rules: the encoding rule, the rule of internal change, and the quantization rule. In our system, the data encoding is to store an image in a stable distribution of a PIFS. Given an input image f (epsilon) F, one can find a PIFS t (epsilon) T such that the equilibrium distribution of this PIFS is the given image f. Therefore, the input image, f, is encoded into a specification of a PIFS, t. This mapping from F (image space) to T (parameter space of PIFS) defines fractal transformation. Fractal transformation encodes an input image into a relatively small vector which catches the characteristics of the input vector. The internal space T is the parameter space of PIFS. The internal change rule of our system uses a local minima algorithm to encode the input data. The output data of the encoding stage is a specification of a stochastic dynamical system. The quantization rule divides the internal data space T by sample data.

Paper Details

Date Published: 1 December 1993
PDF: 13 pages
Proc. SPIE 2060, Vision Geometry II, (1 December 1993); doi: 10.1117/12.165014
Show Author Affiliations
Ying Liu, Savannah State College (United States)


Published in SPIE Proceedings Vol. 2060:
Vision Geometry II
Robert A. Melter; Angela Y. Wu, Editor(s)

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