A framework for contrast enhancement by dyadic wavelet analysis

Laine, Andrew F.; Schuler, Sergio

This paper describes a method of pattern recognition targeted for recognizing complex annotations found in paper documents. Our investigation is motivated by the high reliability required for accomplishing autonomous interpretation of maps and engineering drawings. Our approach includes a strategy based on multiscale representations obtained by hexagonal wavelet analysis. A feasibility study is described in which more than 10,000 patterns were recognized with an error rate of 2.06% by a neural network trained using multiscale representations from a class of 52 distinct patterns. We observed a 21-fold reduction in the amount of information needed to represent each pattern for recognition. These results suggest that high reliability is possible at a reduced cost of representation.


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Academic Units
Biomedical Engineering
Published Here
August 18, 2010


Proceedings : 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition : June 21-23, 1994, Seattle, Washington (Los Alamitos, Calif. : IEEE Computer Society Press, 1994), pp. 740- 745.