Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Theses/Dissertations

Statistics and Probability

2018

Computer vision

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Anisotropic Kernel Smoothing For Change-Point Data With An Analysis Of Fire Spread Rate Variability, John Ronald James Thompson Nov 2018

Anisotropic Kernel Smoothing For Change-Point Data With An Analysis Of Fire Spread Rate Variability, John Ronald James Thompson

Electronic Thesis and Dissertation Repository

Wildland fires are natural disturbances that enable the renewal of forests. However, these fires also place public safety and property at risk. Understanding forest fire spread in any region of Canada is critical to promoting forest health, and protecting human life and infrastructure. In 2014, Ontario updated its Wildland Fire Management Strategy, moving away from ``zone-based" decision making to ``appropriate response" decision making. This new strategy calls for an assessment of the risks and benefits of every wildland fire reported in the province. My research places the emphasis on the knowledge and understanding of fire spread rates and their variabilities. …


Modeling And Mapping Location-Dependent Human Appearance, Zachary Bessinger Jan 2018

Modeling And Mapping Location-Dependent Human Appearance, Zachary Bessinger

Theses and Dissertations--Computer Science

Human appearance is highly variable and depends on individual preferences, such as fashion, facial expression, and makeup. These preferences depend on many factors including a person's sense of style, what they are doing, and the weather. These factors, in turn, are dependent upon geographic location and time. In our work, we build computational models to learn the relationship between human appearance, geographic location, and time. The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by our framework, and several generative and discriminative models that use our dataset to learn the relationship …