Date of Graduation
5-2016
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Engineering
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Gashler, Michael
Committee Member/Reader
Wu, Xintao
Committee Member/Second Reader
Parkerson, James
Abstract
Training a system of artificial neural networks on digital images is a big challenge. Often times digital images contain a large amount of information and values for artificial neural networks to understand. In this work, the inference model is proposed in order to absolve this problem. The inference model is composed of a parameterized autoencoder that endures the loss of information caused by the rescaling of images and transition model that predicts the effect of an action on the observation. To test the inference model, the images of a moving robotic arm were given as the data set. The inference model successfully reconstructed the observation using small, rescaled images and even anticipated the observation based on its intuition using its transition model. Capabilities of the inference model implies that the model extracted the essential features of the digital images.
Citation
Lee, S. H. (2016). Inferring Intrinsic Beliefs of Digital Images Using a Deep Autoencoder. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/36