Volume 7, Issue 4
Regression Analysis on Tie-dye Technique and Pattern Feature

Suqiong Liu , HuieLiang, Weidong Gao, Wei Xue & Ming Gu

Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 561-571.

Published online: 2014-07

Preview Purchase PDF 1 1806
Export citation
  • Abstract

Based on computer vision technology, we studied predictive method of tie-dye pattern information. We extracted the average value of HSV (hue, saturation, value) tri-component of valid tie-dye area, proportion of tie-dye white area and coarseness as pattern feature, and designed correlation analysis on tie-dye production process and pattern feature accordingly. The results showed that dye concentration and pattern feature are highly correlated and the speed is also an important indicator of the effect of tie- dye pattern. In view of tie-dye production speed, concentration process parameters and pattern feature linear regression analysis, the findings are as follows: there is a positive correlation between process parameters and H, S component mean; process parameters negatively correlate with V component mean and proportion of tie-dye white area and coarseness; R-Squared values of prediction model are greater than 0.5. The linear regression models can be used to predict tie-dye image pattern effects.

  • Keywords

Tie-dye Image Processing HSV Regression Analysis

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JFBI-7-561, author = {}, title = {Regression Analysis on Tie-dye Technique and Pattern Feature}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2014}, volume = {7}, number = {4}, pages = {561--571}, abstract = {Based on computer vision technology, we studied predictive method of tie-dye pattern information. We extracted the average value of HSV (hue, saturation, value) tri-component of valid tie-dye area, proportion of tie-dye white area and coarseness as pattern feature, and designed correlation analysis on tie-dye production process and pattern feature accordingly. The results showed that dye concentration and pattern feature are highly correlated and the speed is also an important indicator of the effect of tie- dye pattern. In view of tie-dye production speed, concentration process parameters and pattern feature linear regression analysis, the findings are as follows: there is a positive correlation between process parameters and H, S component mean; process parameters negatively correlate with V component mean and proportion of tie-dye white area and coarseness; R-Squared values of prediction model are greater than 0.5. The linear regression models can be used to predict tie-dye image pattern effects.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi12201409}, url = {http://global-sci.org/intro/article_detail/jfbi/4810.html} }
TY - JOUR T1 - Regression Analysis on Tie-dye Technique and Pattern Feature JO - Journal of Fiber Bioengineering and Informatics VL - 4 SP - 561 EP - 571 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi12201409 UR - https://global-sci.org/intro/article_detail/jfbi/4810.html KW - Tie-dye KW - Image Processing KW - HSV KW - Regression Analysis AB - Based on computer vision technology, we studied predictive method of tie-dye pattern information. We extracted the average value of HSV (hue, saturation, value) tri-component of valid tie-dye area, proportion of tie-dye white area and coarseness as pattern feature, and designed correlation analysis on tie-dye production process and pattern feature accordingly. The results showed that dye concentration and pattern feature are highly correlated and the speed is also an important indicator of the effect of tie- dye pattern. In view of tie-dye production speed, concentration process parameters and pattern feature linear regression analysis, the findings are as follows: there is a positive correlation between process parameters and H, S component mean; process parameters negatively correlate with V component mean and proportion of tie-dye white area and coarseness; R-Squared values of prediction model are greater than 0.5. The linear regression models can be used to predict tie-dye image pattern effects.
Suqiong Liu , HuieLiang, Weidong Gao, Wei Xue & Ming Gu. (2019). Regression Analysis on Tie-dye Technique and Pattern Feature. Journal of Fiber Bioengineering and Informatics. 7 (4). 561-571. doi:10.3993/jfbi12201409
Copy to clipboard
The citation has been copied to your clipboard