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, …

Alzheimer’S Disease Detection Using Convolutional Neural Netowork & Wavelets, Ean Hendrickson, Gabriel Amancio, Chloe Eusbio, Kaioli Bessert

Electrical Engineering

It is estimated that 5.7 million people in the United States have Alzheimer’s dementia, including about 1 in 10 people above the age of 65. Alzheimer’s disease detection can help doctors to diagnose patients earlier and help them with preventative, proactive treatment. In this project, we attempt to use two different methods to analyze MRI brain images for Alzheimer’s disease detection, one with convolutional neural network and deep learning algorithm, and the other one with discrete wavelet transform. Computer simulation results are presented in the report.

Comparing Data Augmentation Strategies For Deep Image Classification, Sarah O'Gara, Kevin Mcguinness

Session 2: Deep Learning for Computer Vision

Currently deep learning requires large volumes of training data to fit accurate models. In practice, however, there is often insufficient training data available and augmentation is used to expand the dataset. Historically, only simple forms of augmentation, such as cropping and horizontal flips, were used. More complex augmentation methods have recently been developed, but it is still unclear which techniques are most effective, and at what stage of the learning process they should be introduced. This paper investigates data augmentation strategies for image classification, including the effectiveness of different forms of augmentation, dependency on the number of training examples, and …

Deep Convolutional Neural Networks For Estimating Lens Distortion Parameters, Sebastian Lutz, Mark Davey, Aljosa Smolic

Session 2: Deep Learning for Computer Vision

In this paper we present a convolutional neural network (CNN) to predict multiple lens distortion parameters from a single input image. Unlike other methods, our network is suitable to create high resolution output as it directly estimates the parameters from the image which then can be used to rectify even very high resolution input images. As our method it is fully automatic, it is suitable for both casual creatives and professional artists. Our results show that our network accurately predicts the lens distortion parameters of high resolution images and corrects the distortions satisfactory.