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Proceedings Paper

Adaptive learning systems and qualitative manipulation of digital imagery
Author(s): John C. Dalton
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Paper Abstract

Recent developments in adaptive learning systems allow quantifying of a user's qualitative aesthetics and provide an alternative to more traditional approaches to image manipulation. Image enhancement or other desired manipulations can be thought of as nonlinear transformations from an input space of arbitrary images into an output space of desired aesthetic images. Derivation of imaging manipulations of this type can be cast as supervised learning problems. Approaches to reduce the dimensionality of the transformations described above are highly desirable. One approach is to define transformations through more structured descriptors than raw image pixels. Transformations are then learned between sets of image metrics as opposed to sets of image pixels. Adaptive neural networks can be used to learn arbitrary imaging transformations from example images. An alternative approach that is functionally equivalent is to use an adaptive fuzzy logic controller. Fuzzy logic can be thought of as a linguistically understandable meta-representation of an underlying functional transformation. Fuzzy logic also provides a possible link between semantic labeling of qualitative image characteristics and the underlying raw image data.

Paper Details

Date Published: 1 May 1994
PDF: 12 pages
Proc. SPIE 2179, Human Vision, Visual Processing, and Digital Display V, (1 May 1994); doi: 10.1117/12.172694
Show Author Affiliations
John C. Dalton, Apple Computer Advanced Technology Group (United States)

Published in SPIE Proceedings Vol. 2179:
Human Vision, Visual Processing, and Digital Display V
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

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