Automatic Generation Technology of Scrap Denim Clothes in Quilting Art Patterns by Integrating Computer Vision.

Abstract

The combination of computer vision and advanced Fuzzy C-Means algorithms, such as Hybrid OK-Means Fuzzy C-Means (HOFCM), provides a revolutionary way to automatically generate technology of Scrap Denim Clothes in Quilting Art Patterns. The research focuses on the creation of an automatic generating technique specifically designed for making quilting designs from scrap denim clothing. This study investigates the effectiveness of HOFCM in improving the process of creating quilted art patterns from discarded denim materials. Extensive experimentation and analysis have shown that HOFCM dramatically decreases computational time while improving clustering solution quality, particularly when dealing with huge, high-dimensional datasets like scrap denim cloth analysis. The study emphasizes HOFCM's potential to speed up the quilting art pattern generation process, providing designers and artists with a more efficient and precise way to transform old denim clothing into visually appealing designs. The findings highlight HOFCM's transformative impact on automatic generation technology for quilting art patterns, paving the path for innovation and originality in textile art and design. HOFCM significantly lowers time by up to 93.94% compared to regular Fuzzy C-Means on four real and one synthetic datasets. The worst-case scenario resulted in a 2.51% reduction in solution quality.