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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Deep Learning-Based Automatic Stereology For High- And Low-Magnification Images, Hunter Morera
Deep Learning-Based Automatic Stereology For High- And Low-Magnification Images, Hunter Morera
USF Tampa Graduate Theses and Dissertations
Quantification of the true number of stained cells in specific brain regions is an important metric in many fields of biomedical research involving cell degeneration, cytotoxicology, cellular inflammation, and drug development for a wide range of neurological disorders and mental illnesses. Unbiased stereology is the current state-of-the-art method for collecting the cell count data from tissue sections. These studies require trained experts to manually focus through a z-stack of microscopy images and count (click) on a hundred or more cells per case, making this approach time consuming (~1 hour per case) and prone to human error (i.e., inter-rater variability). Thus, …
Facial Expression Recognition Using Convolutional Neural Networks (Cnns) And Generative Adversarial Networks (Gans) For Data Augmentation And Image Generation, Shekhar Singh
UNLV Theses, Dissertations, Professional Papers, and Capstones
Facial expressions play a crucial role in human communication, serving as a powerful means to convey emotions. However, classifying facial expressions using artificial intelligence (AI) can be challenging, especially with small datasets and images. Facial Expression Recognition (FER) is an active area of research, with Convolutional Neural Networks (CNNs) being widely employed for classification. In this research, we propose a CNN-based approach for FER that utilizes both original and augmented datasets to enhance classification accuracy. Experimental results on the FER2013 dataset show test accuracies of 63.39% and 64.59% for the original and augmented datasets, respectively, in a seven-class classification task. …
Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman
Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman
Graduate Theses and Dissertations
Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …