Doctoral Thesis Defense: Tamara Finide Dittimi
Speaker: Tamara Finide Dittimi
Supervisor: Dr. C. Y. Suen
Examining Committee: Drs. M. El Yacoubi, A. Krzyzak, O. Ormandjieva, Y. Zeng, L. Amador (Chair)
Title: Banknote Authentication and Medical Image Diagnosis Using Feature Descriptors and Deep Learning Methods
Date: Friday, June 28, 2019
Place: EV 3.309
Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. Similarly, many physicians must interpret medical images. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. This thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis.
In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter.
In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors.
In our third research problem, experiments were conducted on a pre-trained mobile app for medical image diagnosis using three convolutional layers with an Ensemble Classifier comprising PCA and bagging of five base learners. Also, we implemented a Bidirectional Generative Adversarial Network to mitigate the effect of the Binary Cross Entropy loss based on a Deep Convolutional Generative Adversarial Network.
Lastly, we proposed a variant of the Single Image Super-resolution for medical analysis by redesigning the Super Resolution Generative Adversarial Network to increase the Peak Signal to Noise Ratio during image reconstruction by incorporating a loss function based on the mean square error of pixel space and Super Resolution Convolutional Neural Network layers.