Feature Detection And Enhancement By A Rotating Kernel Min-Max Transformation

Yim-Kul Lee; William T. Rhodes

doi:10.1117/12.962233

5 February 1990 Feature Detection And Enhancement By A Rotating Kernel Min-Max Transformation

Yim-Kul Lee, William T. Rhodes

Proceedings Volume 1151, Optical Information Processing Systems and Architectures; (1990) https://doi.org/10.1117/12.962233
Event: 33rd Annual Technical Symposium, 1989, San Diego, United States

Abstract

A new method is proposed to detect and enhance such features as object bound-aries or line segments in a noisy gray-scale image. This method utilizes directional information at each point in the input image. The input image is convolved with a 2-D kernel, discussed below, which is rotated through 360 degrees, either continuously or discretely in a fairly large number of steps. As the kernel rotates, the convolution output is measured and the maximum, minimum, and mean values at each point (as a function of rotation angle) are stored in a computer. Once these values are obtained, a class of image processing operations can be performed. In an optical implementation of the processing operation, it is necessary to physically rotate a mask in the optical system. However, this is much faster than effecting an equivalent kernel-rotation operation with a digital image processor.

Citation Download Citation

Yim-Kul Lee and William T. Rhodes "Feature Detection And Enhancement By A Rotating Kernel Min-Max Transformation", Proc. SPIE 1151, Optical Information Processing Systems and Architectures, (5 February 1990); https://doi.org/10.1117/12.962233

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