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A Statistical Framework for Detecting Edges from Noisy Fourier Data with Multiple Concentration Factors


Abstract The concentration factor edge detection method was developed to compute the locations and values of jump discontinuities in a piecewise-analytic function from its first few Fourier series coecients. The method approximates the singular support of a piecewise smooth function using an altered Fourier conjugate partial sum. The accuracy and characteristic features of the resulting jump function approximation depends on these lters, known as concentration factors. Recent research showed that that these concentration factors could be designed using aexible iterative framework, improving upon the overall accuracy and robustness of the method, especially in the case where some Fourier data are untrustworthy or altogether missing. Hypothesis testin... (more)
Created Date 2016-05
Contributor Lubold, Shane Michael (Author) / Gelb, Anne (Thesis Director) / Cochran, Doug (Committee Member) / Viswanathan, Aditya (Committee Member) / Economics Program in CLAS / School of Mathematical and Statistical Sciences / Barrett, The Honors College
Subject Statistics / Image Processing / Fourier Analysis
Series Academic Year 2015-2016
Type Text
Extent 27 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS


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