Facet Model and Mathematical Morphology for Surface Characterization
This paper describes an algorithm for the automatic segmentation and representation of surface structures and non-uniformities in an industrial setting. The automatic image processing and analysis algorithm is developed as part of a complete on-line web characterization system of a papermaking process at the wet end. The goal is to: (1) link certain types of structures on the surface of the web to known machine parameter values, and (2) find the connection between detected structures at the beginning of the line and defects seen on the final product. Images of the pulp mixture (slurry), carried by a fast moving table, are obtained using a stroboscopic light and a CCD camera. This characterization algorithm succeeded where conventional contrast and edge detection techniques failed due to a poorly controlled environment. The images obtained have poor contrast and contain noise caused by a variety of sources. After a number of enhancement steps, conventional segmentation methods still f ailed to detect any structures and are consequently discarded. Techniques tried include the Canny edge detector, the Sobel, Roberts, and Prewitt's filters, as well as zero crossings. The facet model algorithm, is then applied to the images with various parameter settings and is found to be successful in detecting the various topographic characteristics of the surface of the slurry. Pertinent topographic elements are retained and a filtered image computed. Carefully tailored morphological operators are then applied to detect and segment regions of interest. Those regions are then selected according to their size, elongation, and orientation. Their bounding rectangles are computed and represented. Also addressed in this paper are aspects of the real time implementation of this algorithm for on-line use. The algorithm is tested on over 500 images of slurry and is found to segment and characterize nonuniformities on all 500 images.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (US)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 14611
- Report Number(s):
- ORNL/CP-104267; TRN: AH200129%%338
- Resource Relation:
- Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 13 Nov 1999
- Country of Publication:
- United States
- Language:
- English
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