Abstract

The rotating-kernel min–max transformation is a nonlinear image-processing operation that can be applied to the enhancement of directional features in noisy images. Associated with a particular transformation are (a) a convolution kernel and (b) a function that maps to a final output value the maximum and minimum values measured at point (x, y) in the convolution output as the kernel rotates through 360°. Frequently used kernels are narrow in one direction and broad in the other, typically with rectangular, triangular, or Gaussian profiles in the long direction. Simple but effective functional mappings include I out(x, y) = [Max(x, y) − Min(x, y)] and I out(x, y) = {1 − [Min(x, y)/Max(x, y)]m}. Improved results are often obtained if successive rotating-kernel min–max transformation operations are performed in cascaded systems. Two binarization procedures based on the rotating-kernel min–max transformation can be used to extract straight-line features from noisy gray-scale images. The effects on the processed image of kernel type and size, mapping function, and binarization scheme are discussed.

© 1995 Optical Society of America

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