We describe an automated image processing approach for detecting and characterizing cavitation pits on stainless steel surfaces. The image sets to be examined have been captured by a scanning electron microscope (SEM). Each surface region is represented by a pair of SEM images, one captured before and one after the cavitation-causing process. Unfortunately, some required surface preparation steps between pre-cavitation and post-cavitation imaging can introduce artifacts and change image characteristics in such a way as to preclude simple image-to-image differencing. Furthermore, all of the images were manually captured and are subject to rotation and translation alignment errors as well as variations in focus and exposure. In the presented work, we first align the pre- and post- cavitation images using a Fourier-domain technique. Since pre-cavitation images can often contain artifacts that are very similar to pitting, we perform multi-scale pit detection on each pre- and post-cavitation image independently. Coincident regions labeled as pits in both pre- and post-cavitation images are discarded. Pit statistics are exported to a text file for further analysis. In this paper we provide background information, algorithmic details, and show some experimental results.

Date Published: 1 May 2003
PDF: 9 pages
Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.515154

Published in SPIE Proceedings Vol. 5132:
Sixth International Conference on Quality Control by Artificial Vision
Kenneth W. Tobin Jr.; Fabrice Meriaudeau, Editor(s)