Toti, GiuliaSutrisno, Raymond2019-01-032019-01-032018-10-18http://hdl.handle.net/10657/3807Contemporary methods to analyze dewetting stages from optical microscopy are limited to manual classification. The project seeks to automate this process by using image processing techniques and machine learning. Magnitude independent features, such as pixel skew, variance, and entropy, along with their local deviations, were used to train a simple feed forward neural network. From a dataset of 64 images, tuning was achieved by selecting the neural network hyperparameter configuration with the highest peak cross validation score. The selected model accurately classified approximately 80% of the testing set.en-USThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Poster