文章摘要
聚类分析提取PCA变换后的火焰图像颜色特征
Extracting the flame image color feature after PCA transformation based on clustering analysis
  
DOI:
中文关键词: 火焰图像  颜色特征  聚类分析  炼钢终点控制
英文关键词: flame image  color feature  clustering analysis  steel-making endpoint control
基金项目:国家自然科学基金资助项目(61272063); 湖南省高校创新平台开放基金项目(13K087)
作者单位
燕梅,林剑,郭小玉,王静 湖南科技大学 知识处理与网络化制造湖南省普通高校重点实验室湖南 湘潭 411201 
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中文摘要:
      炉口火焰颜色特征是判断炼钢终点碳及终点温度的重要特征.为实现实时控制,通常利用PCA变换缩短图像特征提取时间,由于PCA变换存在误差,难以在变换后的图像中提取独立颜色,文中首先在原始火焰图像中选取各类颜色样本,计算颜色变换前后的类内距和类间距,发现图像变换前后颜色的聚类性能具有一致性,然后利用聚类方法确定变换后的颜色窗口大小,通过颜色窗口提取序列图像颜色像元数量随时间变换特征,采用A,B 两炉冶炼同一型号钢种图像数据进行实验,实验结果表明:通过聚类分析的方法所提取的颜色特征能较好地反映转炉炼钢物理化学过程.
英文摘要:
      Combustion flame plays an important role in predicting endpoint contents of carbon and steel temperature’s in a basic oxygen furnace (BOF). Since the results of principal component analysis (PCA) exist errors, it was difficult to extract independent colors from the converted images. Different kinds of color swatch were selected from original flame images. By calculating the colors’ within-class distance and class distance between before and after transformation, the colors’ clustering performance was found that it keep consistent. Clustering method was used to determine color’s window size after PCA transformation. Through colors’ window, the characteristic of pixel number of sequence images change with time were extracted. Image data from two furnace A and B, smelted same type of steel, was used to make experiment. Results show that the color features, extracted by clustering analysis, is able to much better reflect the physical and chemical processes in steel-making.
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