Publication Date
12-2016
Date of Final Oral Examination (Defense)
10-27-2016
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Computer Science
Department
Computer Science
Supervisory Committee Chair
Tim Andersen, Ph.D.
Supervisory Committee Member
Edoardo Serra, Ph.D.
Supervisory Committee Member
Aaron Westcott
Abstract
This work investigates the feasibility of using non-destructive testing, in particular radiation tomography, to recover length statistics from aggregates of fried batonnet cut potato. Non-destructive testing comprises a variety of useful techniques for determining properties of an object that might otherwise require altering or destroying the object physically. Tomography is a common form of non-destructive testing used primarily to infer properties internal to an object. This process involves exposing the object of interest to radiation and detecting the quantity of radiation energy that penetrates the object, usually resulting in a grey scale image.
To do this, an artificial data pipeline is developed in order to obtain annotated examples. This pipeline allows faster data collection than can be done in a real production environment coupled with the ability to control all aspects of the resulting images. Additionally, these examples are used to train a convolutional neural network, a widely successful machine learning algorithm for image processing. The network learns a relation between the images and the length estimates and can then be used to provide length estimates on novel examples.
Results show that with careful preparation and enough expected variation in the product being inspected, the image resulting from radiation tomography contains enough information to recover estimates of the lengths of the product with significantly less expected error than a naive baseline.
Recommended Citation
Lovitt, Jesse, "Estimating Length Statistics of Aggregate Fried Potato Product via Electromagnetic Radiation Attenuation" (2016). Boise State University Theses and Dissertations. 1211.
https://scholarworks.boisestate.edu/td/1211
documentclass file for the tex thesis document