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- All Subjects: Imaging
- Creators: Baerwaldt, Morgan Kathleen
Description
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. Furthermore, the concentration factor method yields some insight into the detection of edges in spectral phase data. An iterative design approach was taken to realize an edge detector using only the spectral phase data, also allowing for the design of an edge detector when phase data are intermittent or corrupted. Problem formulations showing the power of the design approach are given throughout. A post-processing scheme relying on the difference of multiple edge approximations yields a strong edge detector which is shown to be resilient under noisy, intermittent phase data. Lastly, a thresholding technique is applied to give an explicit enhanced edge detector ready to be used. Examples throughout are demonstrate both on signals and images.
ContributorsReynolds, Alexander Bryce (Author) / Gelb, Anne (Thesis director) / Cochran, Douglas (Committee member) / Viswanathan, Adityavikram (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The NASA Psyche Iron Meteorite Imaging System (IMIS) is a standalone system created to image metal meteorites from ASU’s Center for Meteorite Studies’ collection that have an etched surface. Meteorite scientists have difficulty obtaining true-to-life images of meteorites through traditional photography methods due to the meteorites’ shiny, irregular surfaces, which interferes with their ability to identify meteorites’ component materials through image analysis. Using the IMIS, scientists can easily and consistently obtain glare-free photographs of meteorite surface that are suitable for future use in an artificial intelligence-based meteorite component analysis system. The IMIS integrates a lighting system, a mounted camera, a sample positioning area, a meteorite leveling/positioning system, and a touch screen control panel featuring an interface that allows the user to see a preview of the image to be taken as well as an edge detection view, a glare detection view, a button that allows the user to remotely take the picture, and feedback if very high levels of glare are detected that may indicate a camera or positioning error. Initial research and design work were completed by the end of Fall semester, and Spring semester consisted of building and testing the system. The current system is fully functional, and photos taken by the current system have been approved by a meteorite expert and an AI expert. The funding for this project was tentatively capped at $1000 for miscellaneous expenses, not including a camera to be supplied by the School of Earth and Space Exploration. When SESE was unable to provide a camera, an additional $4000 were allotted for camera expenses. So far, $1935 of the total $5000 budget has been spent on the project, putting the project $3065 under budget. While this system is a functional prototype, future capstone projects may involve the help of industrial designers to improve the technician’s experience through automating the sample positioning process.
ContributorsBaerwaldt, Morgan Kathleen (Author) / Bowman, Cassie (Thesis director) / Kozicki, Michael (Committee member) / School of Art (Contributor) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05