Date of Graduation


Document Type


Degree Name

Bachelor of Science in Computer Engineering

Degree Level



Computer Science and Computer Engineering


Gashler, Michael

Committee Member/Reader

Wu, Xintao

Committee Member/Second Reader

Parkerson, James


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.