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Full-Text Articles in Signal Processing
Object Detection And Classification In The Visible And Infrared Spectrums, Domenick D. Poster
Object Detection And Classification In The Visible And Infrared Spectrums, Domenick D. Poster
Graduate Theses, Dissertations, and Problem Reports
The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark …
An Analysis On Adversarial Machine Learning: Methods And Applications, Ali Dabouei
An Analysis On Adversarial Machine Learning: Methods And Applications, Ali Dabouei
Graduate Theses, Dissertations, and Problem Reports
Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to …
Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani
Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani
Graduate Theses, Dissertations, and Problem Reports
Deep models have provided high accuracy for different applications such as person recognition, image segmentation, image captioning, scene description, and action recognition. In this dissertation, we study the deep learning models and their application in improving the performance and reliability of person recognition. This dissertation focuses on five aspects of person recognition: (1) multimodal person recognition, (2) quality-aware multi-sample person recognition, (3) text-independent speaker verification, (4) adversarial iris examples, and (5) morphed face images. First, we discuss the application of multimodal networks consisting of face, iris, fingerprint, and speech modalities in person recognition. We propose multi-stream convolutional neural network architectures …