DISSERTATION DEFENSE

Author : Jun Zhou

Advisor : Dr. Song Wang

Date : May 24, 2022

Time 9:00 am

Place : Virtual (Teams link below)

The Teams invite link is here

Abstract

A challenging problem in modern archaeology is to identify and reconstruct full decorative curve pattern designs from fragmented heritage objects, such as the pottery sherds from southeastern North America. The difficulties of this problem lie in 1) these pottery sherds are usually fragmented so that each sherd only covers a small portion of its underlying full design; 2) these sherds can be highly degraded that curves may contain missing segments or become very shallow; and 3) curve patterns on sherd surfaces may overlap, resulting in composite patterns. Abstracted from this archaeological problem, two computer vision problems are studied: design identification for identifying underlying full design on a sherd by curve pattern matching and sherd identification for grouping unidentified sherds for new design discovery by curve pattern clustering. For design identification, two new curve pattern matching methods are proposed, a Chamfer matching based method for composite pattern matching and a patch-based matching method for noisy curve patterns and composite patterns by deep metric learning and region growing. For sherd identification, a new curve pattern clustering method is proposed involving curve pattern similarity matrix building by deep feature learning, graph partition and iterative cluster refinement. An archaeological computer-aided system, called Snowvision, is developed in this research. The proposed algorithms frame the core of Snowvision.