Michael Sieverts

Roadway Sign Detection and Classification

This was a capstone project for the Deep Learning Certificate at the University of Utah. In this project, a team of three graduate students partnered with Blyncsy Inc. to solve a relevant problem. Our team developed an approach to detect and classify roadway signs in dashcam images. Our solution consisted of two stages, detection using Faster R-CNN and classification using EfficientNet..

A key challenge in this problem was obtaining representative data for over 600 classes of roadway signs. To overcome this obstacle, we generated synthetic data using standard example images of roadway signs from the MUTCD. This approach achieved modest results with relatively little development time.

This project was selected for a podium presentation at the IEEE MFI conference

Deep Learning For Image Denoising & Segmentation

Deep learning has proven to be an incredibly useful tool in image processing. This project used a convolutional neural network to perform both denoising (regression) and segmentation tasks on micro CT data. We used the U-net architechture presented by Ronneberger to process the images. I developed a custom implementation of the U-net in Pytorch to perform the denoising and segmentation tasks.

The code for the U-net can be viewed here.

Light-Weight Machine Learning Library

While enrolled in the Machine Learning course (CS 6350) at the University of Utah, I developed a lightweight machine learning library. The library is written in Python and includes implementations of the following algorithms:

• Decision Tree
• Adaboost
• Bagging
• Random Forest
• Linear Regression
• Perceptron
• Support Vector Machine
• Logistic Regression
• Neural Network

The code for this lightweight library can be viewed here.

SPIE: Incorporating machine learning with Raman spectroscopy to differentiate bone types

I had the opportunity to present experimental work at the SPIE conference in 2020 about using Raman spectroscopy and machine learning (ML) to differentiate bone types. This project used Raman spectra of similar tissues as input to ML algorithms to learn their classification. The samples included rat long bone, rabbit long bone, and rabbit crania.

I tested the effect of different data processing and techniques before classification. These approaches included using intensities at all wavenumbers, dimension reduction through principal component analysis (PCA), and feature extraction using domain knowledge.

I tested the performance of a variety of ML classification algorithms on the bone samples. This project's most successful classification approach included extracting features according to domain knowledge and performing the classification with support vector machine. This analysis was performed using R.

The abstract for this project can be found here.

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while (!deck.isInOrder()) {
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}

print 'It took ' + i + ' iterations to sort the deck.';

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