Rapid vision-based system for secondary copper content estimation

摘要: Abstract A vision-based color analysis system was developed for rapid estimation of copper content in the secondary smelting process. Firstly, cross section images samples were captured by designed vision system. After preprocessing and segmenting procedures, selected according to their grayscale standard deviations pixels percentages edge luminance component. The then used extract information improved vector angles, from which model based on least squares support regression (LSSVR) method. For comparison, three additional LSSVR models, namely, only with sample selection, angle, without selection or developed. In addition, two exponential Experimental results indicate that proposed method is more effective improving accuracy, particularly when size small.