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Edge Detection from Spectral Phase Data


Abstract The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor edge detection method was therefore developed to realize an edge detector directly from spectral data.

This thesis explores the possibilities of detecting edges from the phase of the spectral data, that is, without the magnitude of the sampled spectral data. Prior work has demonstrated that the spectral phase contains particularly important information about underlying features in a signal... (more)
Created Date 2016-05
Contributor Reynolds, Alexander Bryce (Author) / Gelb, Anne (Thesis Director) / Cochran, Dogulas (Committee member) / Viswanathan, Adityavikram (Committee member) / School of Mathematical and Statistical Sciences () / Barrett, The Honors College ()
Subject edge detection / jump detection / signal processing / imaging / radar
Series Academic Year 2015-2016
Type Text
Extent 33 pages
Language English
Rights All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS


  areynolds_thesis_041516.pdf
2.7 MB application/pdf
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