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1 December 1993 Continuous-tone image recognition using fractal theory
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Proceedings Volume 2060, Vision Geometry II; (1993)
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States
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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ying Liu "Continuous-tone image recognition using fractal theory", Proc. SPIE 2060, Vision Geometry II, (1 December 1993);


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