Rotating-kernel min-max algorithms for straight-line feature enhancement.
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.
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