NDE data fusion using morphological approaches

Abstract

This manuscript has been reproduced from the microfihn master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter &ce, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. £Bgher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI A Bell & Howell Infonnation Company 300 North Zed> Road, Ann Arbor MI 48106-1346 USA 313/761-4700 800/521-0600 NDE data fusion using morphological approaches by Young-Won Song A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of

Data fusion for NDE signal characterization

“…This method requires accurate estimates of the degradation kernel and noise statistics. Song [13] employs morphological methods to fuse ultrasonic and eddy current NDE images. The method combines the edge information from an ultrasonic image with information concerning the depth of the defect from an eddy current image using AND operator.…”

Data fusion using expected output membership functions

“…The maximum likelihood (ML) technique finds the values of the parameters that maximize the likelihood (probability functions or joint density functions) of the observed sample [32]. That is, we choose 8 so that for any admissible value 0 L{x\e)> Ux\0).…”

“…Song [32] proposed a data fusion algorithm using morphology and applied this algorithm to the NDE images. This algorithm uses a morphological closing operation to obtain an outline of the defect from the speckle noise reduced ultrasonic image.…”

“…Eddy current images can give information about the existence of defects and the depths of the defects. Therefore, the effectiveness of this fusion is the combination of the excellent edges from ultrasonic image with the depth of the defect from eddy current image [32]. Figure 8.1 shows the data fusion scheme for heterogeneous sensors that has three parts: preprocessing, separation, and fusion.…”

“…When the inputs are cooperative, the highest gray level of the eddy current image is used for the gray level of the fused image [32]. The adaptive rules are proposed for the sizes of the fiizzy sets.…”