Physical Sciences and Mathematics Commons^™

Learning Representations For Human Identification, Sinan Sabri

Graduate Theses, Dissertations, and Problem Reports

Long-duration visual tracking of people requires the ability to link track snippets (a.k.a. tracklets) based on the identity of people. In lack of the availability of motion priors or hard biometrics (e.g., face, fingerprint, or iris), the common practice is to leverage soft biometrics for matching tracklets corresponding to the same person in different sightings. A common choice is to use the whole-body visual appearance of the person, as determined by the clothing, which is assumed to not change during tracking. The problem is challenging because distinct images of the same person may look very different, since no restrictions are …

Fashion Compatibility Prediction Using Ensemble Learning, Nathan Utzman

Graduate Theses, Dissertations, and Problem Reports

Fashion is important both financially and for self-expression. There are many tasks in the fashion domain which can be addressed with artificial intelligence. The task of fashion compatibility prediction is to determine how well a set of items work together to form an outfit. Two main tasks are typically used to evaluate the performance of a fashion compatibility prediction model – Outfit Compatibility Prediction and Fill in the Blank.

In this work, a compatibility prediction model, which is based on the graph autoencoder, is evaluated. This same model is then used in a homogeneous ensemble learning approach, proposed to improve …

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 …

Representation Learning With Adversarial Latent Autoencoders, Stanislav Pidhorskyi M.S.

Graduate Theses, Dissertations, and Problem Reports

A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.
Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wise
similarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon …

A Machine Learning Approach To Estimate The Annihilation Photon Interactions Inside The Scintillator Of A Pet Scanner, Sai Akhil Bharthavarapu

Graduate Theses, Dissertations, and Problem Reports

Biochemical processes are chemical processes that occur in living organisms. They can be studied with nuclear medicine through the help of radioactive tracers. Based on the radioisotope used, the photons that are emitted from the body tissue are either detected by single-photon emission computed tomography (SPECT) or by positron emission tomography (PET) scanners. SPECT uses gamma rays as tracer but gives a weaker contrast and spatial resolution compared to a PET scanner which uses positrons as tracer. PET scans show the metabolic changes occurring at the cellular level in an organ or a tissue. This detection is important because diseases …

A Deep Dive Into The Land Development Dynamics Of A Complex Landscape, Pariya Pourmohammadi

Graduate Theses, Dissertations, and Problem Reports

Land development is a complex and dynamic process simultaneously interacting with numerous environmental, cultural and economic procedures. In this research we studied past, present and future of land transformation in Appalachia. This dissertation is organized in three-essay format and each essay is focused on one aspect of land development processes in a sub-region in the Appalachian region. In the first essay, deep learning techniques are used to build predictive models for the land development. This study presets deconvolutional neural networks models in predicting land development. On the second essay, spatial data analysis and remote sensing are used to investigate the …

Quantifying Human Biological Age: A Machine Learning Approach, Syed Ashiqur Rahman

Graduate Theses, Dissertations, and Problem Reports

Quantifying human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this work, we first introduce a new anthropometric measure (called Surface-based Body Shape Index, SBSI) that accounts for both body shape and body size, and evaluate its performance as a predictor of all-cause mortality. We analyzed data from the National Health and Human Nutrition Examination Survey (NHANES). Based on the analysis, we introduce a new body shape index constructed from four important anthropometric determinants of body shape and body size: body …