关键词: 火焰图像, 碳含量预测, 多尺度非均匀采样, 像素变化趋势, 彩色纹理特征

Abstract: In the process of converter steelmaking,there is a close relationship between the color and texture of converter mouth flame and the carbon content of molten steel at the endpoint .To extract effective flame image features for accurately predicting the carbon content of the molten steel,an Improved Multi-trend Binary Coded Descriptor method (IMTBCD) was proposed by combining multi-scale with multi-direction characteristics of the flame textures.Through the color channel fusion strategy with the correlation between color channels,the color texture representation of the flame image was obtained.The multi-scale non-uniform sampling strategy was used to select sampling points in each scale range to construct the multi-scale expression of color textures.According to the different changing of the sampling points from the center point symmetry direction and the diagonal symmetry direction,the color texture features were obtained through multi-trend coding.With the extracted color texture features,the carbon content was predicted via Generalized Regression Neural Network (GRNN) regression model,and the experiment showed that the accuracy of carbon content prediction was 95.7% within the error range of 0.02%.

Key words: flame images, carbon content prediction, multi-scale non-uniform sampling, pixels change trend, color texture features