Automating Interpretation of Images and Visual Inspections in Modern Manufacturing and Medical Settings
Metadata Field | Value | Language |
---|---|---|
dc.contributor.advisor | Silva, Daniel | |
dc.contributor.author | Rasoolian, Behnam | |
dc.date.accessioned | 2021-01-08T19:18:05Z | |
dc.date.available | 2021-01-08T19:18:05Z | |
dc.date.issued | 2021-01-08 | |
dc.identifier.uri | https://etd.auburn.edu//handle/10415/7558 | |
dc.description.abstract | Automating visual inspection is rapidly finding its way into non-invasive industrial and medical processes. In this work, we address detection automation opportunities in the task of interpreting both medical and industrial images, we combine domain knowledge with state-of-the-art, domain-independent methods such as Convolutional Neural Networks (CNN) to address them. Specifically, we address two problems in additive manufacturing and one in medical imaging as described below. We provide a method to improve the resolution of and automate the extraction of local and global porosity features from XCT images of additively manufactured Ti-6Al-4V. We also address the automation issues in characterizing local roughness features, particularly the curvature of microscopic surface pits observed in microscopy images of AM Ti-6Al-4V. Finally, we developed a deep learning model to automate the task of detection and segmentation of different muscles in images obtained from MRI of the lower back. | en_US |
dc.subject | Industrial and Systems Engineering | en_US |
dc.title | Automating Interpretation of Images and Visual Inspections in Modern Manufacturing and Medical Settings | en_US |
dc.type | PhD Dissertation | en_US |
dc.embargo.status | NOT_EMBARGOED | en_US |
dc.embargo.enddate | 2021-01-08 | en_US |
dc.contributor.committee | Smith, Jeff | |
dc.contributor.committee | Shamsaei, Nima | |
dc.contributor.committee | Sesek, Richard |