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[Defense] Defining and Assessing a Novel Spatio-Chromatic Basis for Unsupervised, Class-Agnostic Scene Segmentation

Thursday, July 22, 2021

10:00 am - 11:30 am

In Partial Fulfillment of the Requirements for the Degree of Master of Science
Terry Hodgson
will defend her thesis
Defining and Assessing a Novel Spatio-Chromatic Basis for Unsupervised, Class-Agnostic Scene Segmentation


Abstract

This thesis defines a novel image feature, the “semantic pattern region”, measures the strength of its correlation to scene semantics, and demonstrates its use for unsupervised, class-agnostic scene segmentation. The semantic pattern region is derived from spatio-chromatic image partitions and has an inherent one-to-one relationship with the semantic regions of the original image.

I will demonstrate the pattern region’s potential utility for segmentation in two ways: 1) by presenting representative examples of the best and worst results produced by the segmentation algorithm, and 2) by reporting the results of a quantitative assessment confirming a strong one-to-one spatio-chromatic correspondence between pattern regions and semantic and salient ground truth regions.


Thursday, July 22, 2021
10:00AM - 11:30AM CT
Online via TBD

Dr. Shishir Shah, thesis advisor

Faculty, students and the general public are invited.