Author
Aydin, M., Agirbas, Asli
Publication Date
2024-06-17
Publication Place
-
Springer Nature
Subject
Star polygons, Deep learning, Mask rcnn, Islamic geometric patterns
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1590-5896
Record ID
f55a3dc5-c875-4b95-b840-c9d11814db23
Library Location
Architecture
Date
2024-06-17
Sample Text
Historical buildings in the Eastern world of architecture host many Islamic geometric patterns which are known as mathematically sophisticated patterns regarding their period of creation. This study focuses on the preparation of a model that can be helpful for the analysis and restoration/maintenance of these patterns. For this, a deep learning model to detect and classify star types in Islamic geometric patterns has been proposed, and the trials were evaluated. Accordingly, this study presents a database containing 5-pointed, 6-pointed, 8-pointed and 12-pointed star types. The database consists of 600 Islamic geometric patterns. A mask RCNN algorithm was trained to detect and classify star types using the prepared database. The results of the training indicate that the loss value is 0.90 and the validation loss value is 0.85. The algorithm was tested using images that it had not seen before and the results were evaluated. This paper presents a discussion on the pros and cons of the trained algorithm.
DOI
10.1007/s00004-024-00789-6
Cilt
26
