Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

The Algorithm for Extracting Elements of National Costume Based on Region Growing

Authors
Deqiang Yang, Jianhou Gan, Bin Wen, Tianwei Xu
Corresponding Author
Deqiang Yang
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.71How to use a DOI?
Keywords
morphology gradient; region growing; costume element; gray discretization algorithm
Abstract
It is difficult to effectively extract costume elements from the national costume image because the structure of costume images is complex and the gray distribution is irregular. Therefore, in this paper, a novel algorithm was proposed for the extraction of the elements using the mathematical morphology method for element internal filling and external pruning. An improved algorithm for region growing image segmentation was proposed to dynamically calculate the mean and variance of seed regions as growth parameters. According to the elements of the gray dispersion, the seeds were divided into three kinds of them to enter the target image which need to be extracted. Different types of seeds played different roles in the growth process. The target element were gradually obtained with each seed growth. The experimental results show that the relatively complete and accurate elements of the costume can be extracted by the algorithm.
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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.71How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Deqiang Yang
AU  - Jianhou Gan
AU  - Bin Wen
AU  - Tianwei Xu
PY  - 2017/06
DA  - 2017/06
TI  - The Algorithm for Extracting Elements of National Costume Based on Region Growing
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 314
EP  - 317
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.71
DO  - https://doi.org/10.2991/caai-17.2017.71
ID  - Yang2017/06
ER  -