Engineering Commons^™

The Importance Of The Instantaneous Phase In Detecting Faces With Convolutional Neural Networks, Luis Armando Sanchez Tapia

Electrical and Computer Engineering ETDs

Convolutional Neural Networks (CNN) have provided new and accurate methods for processing digital images and videos. Yet, training CNNs is extremely demanding in terms of computational resources. Also, for simple applications, the standard use of transfer learning also tends to require far more resources than what may be needed. Furthermore, the final systems tend to operate as black boxes that are difficult to interpret.

The current thesis considers the problem of detecting faces from the AOLME video dataset. The AOLME dataset consists of a large video collection of group interactions that are recorded in unconstrained classroom environments. For the thesis, …

Semantic Image Segmentation Via A Dense Parallel Network, Jiyang Wang

Theses - ALL

Image segmentation has been an important area of study in computer vision. Image segmentation is a challenging task, since it involves pixel-wise annotation, i.e. labeling each pixel according to the class to which it belongs. In image classification task, the goal is to predict to which class an entire image belongs. Thus, there is more focus on the abstract features extracted by Convolutional Neural Networks (CNNs), with less emphasis on the spatial information. In image segmentation task, on the other hand, the abstract information and spatial information are needed at the same time. One class of work in image segmentation …

A Deep Learning Approach To Detect Diabetic Retinopathy In Fundus Images., Winston R. Furtado

Electronic Theses and Dissertations

Background: Diabetic retinopathy is a disease caused due by complications of diabetes mellitus which can lead to blindness. About 33% of the US population with diabetes also show symptoms for diabetes retinopathy. If not treated, diabetic retinopathy worsens over time by progressing through two main pathological stages of non-proliferative and proliferative and four clinical stages. While the diagnostic accuracy of detecting diabetic retinopathy through machine learning have shown to be successful for OCT images, the accuracy of ultra-widefield fundus images have yet to be fully reported. This paper describes a method to non-invasively detect and diagnose diabetic retinopathy from ultra-widefield …