Feature detection and enhancement by a rotating kernel min-max transformation

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Abstract
We present a new hybrid optoelectronic method for nonlinear image processing and demonstrate its application to the enhancement of linear features (e. g. striaghtline segments) in noisy low contrast images. An input image is convolved with a long narrow 2D kernel which is rotated through 360 either continuously or discretely in a large number of steps. The convolution output is measured and the maximum [ Max(x and minimum [ Min(x values at each point (x y) are stored. The output image is then given by some applicationdependent function of Max(x and Min(x We refer to the method to as a rotating kernel mmmax transformation (RKMT). In the enhancement of straightline features two types of kernels are especially useful: (1) a long narrow rectangular profile and (2) a long narrow triangular profile. Calculating the function [Max(x produces significantly enhanced linear features while nondesirable features are suppressed. Better results can be obtained by a cascade system combining a Max(x operation with [Max(x - Min(x Comparison is made with a conventional filtering method. 2.

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