Publication Date

Fall 2016

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Advisor

Christopher Pollett

Keywords

ENAS, Hierarchical Clustering, Jigsaw Puzzle, Mixed-Bag, SEDAS

Subject Areas

Computer science; Artificial intelligence

Abstract

The square jigsaw puzzle is a variant of traditional jigsaw puzzles, wherein all pieces are equal-sized squares; these pieces must be placed adjacent to one another to reconstruct an original image. This thesis proposes an agglomerative hierarchical clustering based solver that can simultaneously reconstruct multiple square jigsaw puzzles. This solver requires no additional information beyond an input bag of puzzle pieces and significantly outperforms the current state of the art in terms of both the quality of the reconstructed outputs as well the number of input puzzles it supports. In addition, this thesis defines Enhanced Direct Accuracy Score (EDAS), Shiftable Enhanced Direct Accuracy Score (SEDAS), and Enhanced Neighbor Accuracy Score (ENAS), which are the first quality metrics specifically tailored for multi-puzzle solvers. This thesis also outlines the first standards for visualizing best buddies and the quality of solver solutions.

Available for download on Sunday, February 04, 2018

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