Ohio State is in the process of revising websites and program materials to accurately reflect compliance with the law. While this work occurs, language referencing protected class status or other activities prohibited by Ohio Senate Bill 1 may still appear in some places. However, all programs and activities are being administered in compliance with federal and state law.

Seminar: Marilyn Vazquez - Non-Parametric Mixture Model Applied to Image Segmentation

Photo of Marilyn Vazquez
March 3, 2020
10:20 am - 11:15 am
MBI Auditorium, Jennings Hall 355

Marilyn Vazquez

Postdoctoral Fellow, MBI


Mixture models have many uses in data science, including clustering and image segmentation. We present an unsupervised image segmentation method applied to natural images. In our research, we model an image as a mixture of color distributions, each coming from a distinct segment in the image. The goal is to estimate the color distributions from all the regions in the image and use them to segment the image. To solve for the distributions, we developed a scheme that solves a subset of the equations in our model, uses least squares to find the missing entries, and then performs coordinate descent to refine the approximation. The parameters in our model are estimated by measuring how well they recover the observed data. We present results on natural images and materials.

This talk is free and open to the public.

Events Filters: