## 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

Full Article | PDF Article**OSA Recommended Articles**

Yim-Kul Lee and William T. Rhodes

Opt. Lett. **15**(23) 1383-1385 (1990)

Yim-Kul Lee and William T. Rhodes

Appl. Opt. **32**(23) 4372-4377 (1993)

D. Kaba, Y. Wang, C. Wang, X. Liu, H. Zhu, A. G. Salazar-Gonzalez, and Y. Li

Opt. Express **23**(6) 7366-7384 (2015)