计算机应用 ›› 2015, Vol. 35 ›› Issue (3): 858-862.DOI: 10.11772/j.issn.1001-9081.2015.03.858

• 虚拟现实与数字媒体 • 上一篇    下一篇

分层自适应的炉内火焰图像显著点提取方法

张晓琳, 崔宁宁, 杨涛, 李洁   

  1. 内蒙古科技大学 信息工程学院, 内蒙古 包头 014010
  • 收稿日期:2014-10-20 修回日期:2014-11-13 出版日期:2015-03-10 发布日期:2015-03-13
  • 通讯作者: 崔宁宁
  • 作者简介:张晓琳(1966-),女,内蒙古包头人,教授,博士,CCF会员,主要研究方向:数据库、图像处理;崔宁宁(1988-),男,河北邯郸人,硕士研究生,主要研究方向:图像处理;杨涛(1989-),男,安徽滁州人,硕士研究生,主要研究方向:图像处理;李洁(1965-),男,内蒙古包头人,副教授,硕士,主要研究方向:系统自动化
  • 基金资助:

    国家自然科学基金资助项目(61164018)

Salient points extraction method of furnace flame image based on hierarchical adaptive algorithm

ZHANG Xiaolin, CUI Ningning, YANG Tao, LI Jie   

  1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou Inner Mongolia 014010, China
  • Received:2014-10-20 Revised:2014-11-13 Online:2015-03-10 Published:2015-03-13

摘要:

针对锅炉和工业生产中产生的大量炉内火焰图像的特征提取问题,提出一种分层自适应显著点提取方法。首先利用块逆概率差模型将原图像转换为块逆概率差(BDIP)图像。在此基础上,将得到的BDIP图像进行Haar小波变换,利用改进的加权方法计算出二维图像的显著值,然后通过提出的自适应的方法构建一棵非平衡四叉树,树的根节点代表整幅图像的显著值,根据每棵子树的显著值占父节点显著值的比例确定子树的显著点数目。该算法与基于BDIP的和基于Haar小波变换的显著点提取算法对比,实验结果表明,边缘准确率和综合特征检索精度都至少提高了10%和3.5%。结果说明,该算法不仅克服了传统显著点提取时数目过多以及提取点不显著的缺点,同时还避免了显著点的局部聚集。

关键词: 显著点, 块逆概率差, 小波变换, 火焰图像, 特征提取

Abstract:

Given the feature extraction of the furnace flame image produced in boilers and industrial production, a hierarchical adaptive method to extract salient points was proposed. First the Block Difference of Inverse Probabilities (BDIP) model was used to change the original image into BDIP image. On the basis of this, the BDIP image was made into Haar wavelet transform, the salient value of two-dimensional image was calculated by the improved weighted method, and then a non-equilibrium quadtree was built through the proposed adaptive method. The root of quadtree represented the salient value of the image, and the salient points number of subtree was determined according to the ratio of the salient value of every subtree to the salient value of parent node. The proposed extracting algorithm was salient points compared with the extracting algorithms based on BDIP and based on Haar wavelet transform. The experimental results show that edge accuracy and comprehensive feature retrieval accuracy at least increase by 10% and 3.5% respectively. The proposed method overcomes the shortcoming of traditional way that it extracts too many salient points and some extracted points are not salient, at the same time the method avoids local gather of salient points.

Key words: salient point, Block Difference of Inverse Probabilities (BDIP), wavelet transform, furnace flame image, feature extraction

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