Abstract: In the process of clothing design and production, the traditional artificial feature element extraction method has the problems of low efficiency and insufficient precision, which is difficult to meet the automation and intelligent needs of modern clothing industry. In order to solve this problem, this paper proposes a technology that combines K-means clustering algorithm and morphology method to extract clothing pattern and line feature elements. This technology uses K-means clustering algorithm to preprocess clothing images to realize feature extraction of clothing pattern elements, and then introduces morphology method to realize feature extraction of image line elements. This technology not only improves the accuracy and efficiency of feature element extraction, but also retains the details of clothing images, which provides a strong support for automatic and intelligent processing in clothing design and production.

Xiaojia Ding, “K-Means and Morphology Based Feature Element Extraction Technique for Clothing Patterns and Lines” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151086

@article{Ding2024,
title = {K-Means and Morphology Based Feature Element Extraction Technique for Clothing Patterns and Lines},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151086},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151086},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Xiaojia Ding}
}