Regression Analysis on Tie-dye Technique and Pattern Feature (Journal of Fiber Bioengineering and Informatics, 2014)
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 ontie-dye production process and pattern feature accordingly. The results showed that dye concentrationand 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 featurelinear regression analysis, the findings are as follows: there is a positive correlation between processparameters and H, S component mean; process parameters negatively correlate with V component meanand proportion of tie-dye white area and coarseness; R-Squared values of prediction model are greaterthan 0.5. The linear regression models can be used to predict tie-dye image pattern effects.