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.
Recommended Citation
Hammoudeh, Zayd, "A Fully Automated Solver for Multiple Square Jigsaw Puzzles Using Hierarchical Clustering" (2016). Master's Theses. 4756.
DOI: https://doi.org/10.31979/etd.3b5y-6ayg
https://scholarworks.sjsu.edu/etd_theses/4756
