記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Soft Computing  

Escher-like tiling attempts to design a tile whose copies cover a plane with no overlaps and no gaps. Deforming a given image shape into a tileable shape while maintaining the original shape as far as possible is a difficult problem. A tileable shape similar to a given image can be generated by an analytical optimization method (AOM) that requires no iterative calculations. However, the generated shape is often non-tileable due to edge self-intersections; moreover, as the method is sensitive to small changes of the input image shape, the input shape must be adjusted manually by much trial and error. To avoid this problem, this paper proposes an Escher-Like Tile design method using Hierarchical OptimizatioN (ELTHON), which divides the tile-design problem and resolves the subproblems by two different methods. The given problem (such as the goal figure shape) is modified by an upper-layer optimizer, and feasible solutions (such as the tileable shapes) are found by a lower-layer optimizer based on AOM. Because the upper layer employs metaheuristics such as a genetic algorithm, which support flexible objective functions and constraints, different types of figure similarity metrics and constraints can be selected to avoid the generation of non-tileable shapes. Experimental results showed that ELTHON designed tiles without violating the constraints, whereas 58.9% of the solutions found by AOM violated the constraints. Additionary, in 16 of 32 figures tested, ELTHON produced tileable shapes with higher similarity to given images compared with AOM, whereas AOM outperformed ELTHON in only two figures.

DOI： 10.1016/j.asoc.2021.107771

Scopus

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Journal of Information Science and Engineering  

This paper proposes a method that deforms a prominent movie or animation character into a tileable shape. Tiling is the act of covering the plane with one or a very few types of figures without overlaps and/or gaps. Although some previous methods can transform a given shape into a tileable shape, they cannot easily move the character into a suitably tileable pose. The proposed method learns the latent feature space that abstracts the target character's silhouettes using a convolutional variational autoencoder, and looks for the poses suitable for tiling by optimization in the latent space. Experimental results showed that the proposed method successfully generated tileable figures of the tested character in various poses, some of which were not included in the training dataset.

DOI： 10.6688/JISE.20210537(3).0005

Scopus

担当区分：筆頭著者   記述言語：日本語   掲載種別：学位論文（博士）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/TAAI48200.2019.8959833

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：英語   会議種別：口頭発表（一般）  

記述言語：日本語   会議種別：口頭発表（一般）  

記述言語：日本語   会議種別：口頭発表（一般）  

記述言語：日本語   会議種別：口頭発表（一般）  

記述言語：日本語   会議種別：ポスター発表  

出願番号：特願2024-049773  出願日：2024年3月

受賞区分：国際学会・会議・シンポジウム等の賞  受賞国：台湾

受賞区分：国内学会・会議・シンポジウム等の賞 

受賞区分：国内学会・会議・シンポジウム等の賞