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在线期刊

烧结断面火焰图像多核Boosting显著性检测

王福斌1), 刘贺飞1), 王蕊1), 何江红1), 武晨2)
1) (华北理工大学电气工程学院 唐山 063210)2) (唐钢国际工程技术股份有限公司 唐山 063000)
分类号: TP391.41 DOI: 10.3724/SP.J.1089.2021.18686
出版年,卷(期):页码: 2021 , 33 ( 9 ): 1466-1474 王福斌
摘要: 烧结断面火焰图像受烟尘与光晕干扰, 图像中火焰边缘与料层区域出现模糊退化现象. 为解决传统基于二维图像特征的显著性检测方法难以有效地获取断面火焰图像实际显著区域的问题, 提出基于边界连通性与多核Boosting的显著性检测方法. 首先在图像颜色空间转换过程中采用色彩去相关原理, 利用边界连通性与暗通道先验获取初始显著性图, 提高初始显著性模型的检测水平; 然后根据断面火焰图像超像素区域自身信息、区域方差和区域对比度构建区域描述符, 在4个尺度上描述超像素分割区域, 并使用基于支持向量回归的多核Boosting算法生成辅助显著性图; 最后将初始显著性图与辅助显著性图进行加权融合, 得到最终显著性图. 以P-R曲线、F-measure、平均绝对误差和单幅图像的运行时间为评价指标, 采用包含人工标注的600幅烧结断面火焰图像将所提方法与其他5种现有方法进行对比, 并对所提方法各阶段进行分析. 实验结果表明, 所提方法优于其他5种方法, 各阶段检测性能逐步增强, 为提高断面火焰图像有效信息的提取精度奠定了基础.
关键词: 烧结火焰图像; 显著性检测; 边界连通性; 支持向量回归; 多核Boosting
Multiple Kernel Boosting Saliency Detection of Flame Image of Sintering Section
Wang Fubin1), Liu Hefei1), Wang Rui1), He Jianghong1), and Wu Chen2)
1) (College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210) 2) (Tang Steel International Engineering Technology Corp, Tangshan 063000)
abstract: The flame image of the sintering section is usually interfered by smoke and halo, which would cause the blurring of flame edge and material layer in the image. In order to solve the problem that traditional saliency detection method based on two-dimensional image features is difficult to effectively obtain the actual saliency of the cross-sectional flame image, a saliency detection method based on boundary connectivity and multi-kernel Boosting is proposed. Firstly, the color de-correlation is used in the process of image color space conversion. Boundary connectivity and dark channel prior principle are used to obtain the initial saliency map. Secondly, the super-pixel region information, regional variance and regional contrast of the flame image are used to construct region descriptor, the multiple kernel Boosting algorithm based on support vector regression is used to generate the complementary saliency map on 4 scales. Finally, the initial saliency map and the complementary saliency map are fused to obtain the final saliency map. 600 flame images including manual labeling are used to compare the proposed method with other 5 existing methods and each stage of the proposed method is analyzed. P-R curve, F-measure, mean absolute error and running time are taken as evaluation indexes. The experimental results show that the proposed method is superior to the other 5 methods, and the detection performance of each stage is gradually enhanced, which lays a foundation for improving the effective information extraction of sintering flame section image.
keyword: sintering flame image; saliency detection; boundary connectivity; support vector regression; multiple kernel Boosting
 
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