Review of Progress in Quantitative Nondestructive Evaluation 1998
DOI: 10.1007/978-1-4615-5339-7_261
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The Applicaton of a Dynamic Threshold to C-Scan Images with Variable Noise

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Cited by 10 publications
(12 citation statements)
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“…Thompson from CNDE, Iowa State University, for their insightful comments and for bringing references [1] and [6] to our attention.…”
Section: Acknowledgmentmentioning
confidence: 99%
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“…Thompson from CNDE, Iowa State University, for their insightful comments and for bringing references [1] and [6] to our attention.…”
Section: Acknowledgmentmentioning
confidence: 99%
“…Therefore, multiple spatial measurements should be incorporated into defect detection and estimation (sizing) algorithms. In [1], measurements within a sliding window were compared with a dynamically chosen threshold in order to detect potential defects in ultrasonic C scans. Related problems have been studied in image processing literature in the context of image segmentation and saliency region detection, see e.g.…”
Section: Introductionmentioning
confidence: 99%
“…In nondestructive evaluation (NDE) applications, defect signal typically affects multiple measurements at neighboring spatial locations and, consequently, multiple spatial measurements should be incorporated into defect identification algorithms [1], [2]. In [1], measurements within a sliding window are compared with a dynamically chosen threshold in order to detect potential defects in ultrasonic C scans.…”
Section: Introductionmentioning
confidence: 99%
“…In [1], measurements within a sliding window are compared with a dynamically chosen threshold in order to detect potential defects in ultrasonic C scans. In [2], we propose a parametric model for defect shape, location, and signal parameters, a hierarchical Bayesian framework and Markov chain Monte Carlo (MCMC) algorithms for estimating these parameters assuming a singe defect, and a sequential method for identifying multiple defect regions.…”
Section: Introductionmentioning
confidence: 99%
“…Figure 3.9 shows the detection result using the original GLR test in Chapter 2 designed without the constraint X t 0. For the peak-to-average SNR method in [15] , the windows Y T and W were swept for one direction from left to right and we computed SNRPA for each location of Y T ·…”
mentioning
confidence: 99%