DOI: https://doi.org/10.15368/theses.2014.114
Available at: https://digitalcommons.calpoly.edu/theses/1269
Date of Award
6-2014
Degree Name
MS in Computer Science
Department/Program
Computer Science
Advisor
John Bellardo
Abstract
Accurately quantifying the human perception of color is an unsolved prob- lem. There are dozens of numerical systems for quantifying colors and how we as humans perceive them, but as a whole, they are far from perfect. The ability to accurately measure color for reproduction and verification is critical to indus- tries that work with textiles, paints, food and beverages, displays, and media compression algorithms. Because the science of color deals with the body, mind, and the subjective study of perception, building models of color requires largely empirical data over pure analytical science. Much of this data is extremely dated, from small and/or homogeneous data sets, and is hard to compare. While these studies have somewhat advanced our understanding of color adequately, mak- ing significant, further progress without improved datasets has proven dicult if not impossible. I propose new methods of crowdsourcing color experiments through color-accurate mobile devices to help develop a massive, global set of color perception data to aid in creating a more accurate model of human color perception.
Included in
Applied Statistics Commons, Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons, Software Engineering Commons