CT Image Sequence Processing For Wood Defect Recognition

Abstract

The research reported in this paper explores a non-destructive testing application of x-ray computed tomography (CT) in the forest products industry. This application involves a computer vision system that uses CT to locate and identify internal defects in hardwood logs. The knowledge of log defects is critical in deciding whether to veneer or to saw up a log, and how to position a log so that the boards sawn from it will have as much clear face as possible. To apply CT to these problems requires efficient and robust computer vision methods. This paper addresses one aspect of the problem of creating such a computer vision system, i.e., the issue of efficient image filtering for suppressing unwanted detail in CT log images. In particular, this paper describes an image filtering method based on a spatial adaptive least squares filter. Simple in structure and efficient in computation, this filter is not based on assumptions about a signal model, but rather on a fixed filtering structure. In conjunction with image segmentation and region growing procedures, the new filter is used in the machine vision system to produce well defined regions that represent areas of potential wood defects.

Citation

Zhu, Dongping; Conners, R.W.; Araman, Philip A. 1991. CT Image Sequence Processing For Wood Defect Recognition. Proceedings, 23rd Southeast Symposium on System Theory