基于边界网格梯度的机体损伤划分评价方法
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摘要
为准确高效的为机体损伤区域划分方法的选择和评价提供决策支持,分析了机体损伤区域的特点,建立了损伤图像划分评价指标——边界网格梯度。并引入熵权法,融合边界网格梯度指标,对TOPSIS方法进行优化改进,建立了完整的机体损伤区域划分评价体系。选用不同灰度熵阈值的飞机机体损伤区域划分图像进行验证。结果表明:该方法有效的表征了边界对损伤邻接区域的划分情况,避免了不同划分图像中边界变化对评价结果的不利影响,评价结果符合视觉感知。
In order to provide decision support for efficiently choosing and evaluating airframe damage region division methods, the characteristic of airframe damage region was analyzed, evaluating indicator of damage image division-boundary grid gradient was established. By introducing entropy method, combining with indicator of boundary grid gradient, the optimization of TOPSIS was presented, and the evaluation system of airframe damage region division was completely established. Airframe damage region division evaluation experiments were performed by different gray level entropy threshold of damage division image. Experimental results showed that, the proposed method efficiently represented division status of damage adjacency region, and avoided the adverse effect of boundary changing in different division image on evaluation. The evaluation result corresponded well with visual perception.
In order to provide decision support for efficiently choosing and evaluating airframe damage region division methods, the characteristic of airframe damage region was analyzed, evaluating indicator of damage image division-boundary grid gradient was established. By introducing entropy method, combining with indicator of boundary grid gradient, the optimization of TOPSIS was presented, and the evaluation system of airframe damage region division was completely established. Airframe damage region division evaluation experiments were performed by different gray level entropy threshold of damage division image. Experimental results showed that, the proposed method efficiently represented division status of damage adjacency region, and avoided the adverse effect of boundary changing in different division image on evaluation. The evaluation result corresponded well with visual perception.
引文
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[2]钱晓华,郭树旭,李雪妍.基于图像理解视角的分割全局平价算法[J].电子学报,2012,10(10):1989-1995.Qian Xiaohua,Guo Shuxu,Li Xueyan.Image Understanding Based Global Evalustion Algorithm for Segmentation[J].Acta Electronica Sinica,2012,10(10):1989-1995.
[3]吴一全,王凯,曹鹏祥.蜂群优化的二维非对称Tsallis交叉熵图像阈值选取[J].智能系统学报,2015,10(1):103-112.Wu Yiquan,Wang Kai,Cao Pengxiang.TwoDimensional Asymmetric Tsallis cross Entropy Image Threshold Selection Using Bee Colony Optimization[J].CAAI Transactions on Intelligent Systems,2015,10(1):103-112.
[4]史普花.基于CT图像的4种肺实质分割算法性能比较研究[D].泰安:泰山医学院,2009.Shi Puhua.Performance Comparative of 4 Lung Parenchyma Segmentation Algorithms Based on CT Images[D].Taian:Taishan Medical University,2009.
[5]闫超德,赵艳坤,郭王,等.基于Delaunay三角网的邻近区域测度及其应用[J].地理与地理信息科学,2013,29(2):125-126.Yan C D,Zhao Y K,Guo W,et al.Neighborhood Measure and Its Application Based on Delaunay Triangulation[J].Geography and Geo-Information Science,2013,29(2):125-126.
[6]Chen H C,Wang S J.The Use of Visible Color Difference in The Quantitative Evaluation of Color Image Segmentation[J].IEEE International Conference on Acoustics(S1520-6149),2004,3(III):593-596.
[7]D Coquin.Quantitative Evaluation of Color Image Segmentation Results using Fuzzy Neural Network[J].International Symposium on Optomechatronic Technologies(S0277-786X),2008,7266(G):1-8.
[8]Zhang Xian,Ming Dongping.Geo-application Oriented Evaluations of Remote Sensing Image Segmentation[J].Cehui Xuebao/Acta Geodaetica et Cartographica Sinica(S1001-1595),2015,44(S1):108-116.
[9]Ming D P,Wang Q,Luo J,et al.Evaluation of High Spatial Resolution Remote Sensing Image Segmentation Algorithms[C]//International Congress on Image and Signal Processing.IEEE,2009:1-5.
[10]Mangalraj P,Singala R,Agrawal A.Evaluation schema for SAR image Segmentation based on Swarm Optimization in Neutrosophic domain[C]//IEEE International Symposium on Signal Processing and Information Technology.IEEE,2015:227-348.
[11]Gavet Yann,Fernandes Mathieu,Debayle Johan,et al.Dissimilarity Criteria and Their Comparison for Quantitative Evaluation of Image Segmentation:Application to Human Retina Vessels[J].Machine Vision and Applications(S0932-8092),2014,25(8):1953-1966.
[12]陈庆为.基于灰色理论的木板材表面缺陷图像分割质量评价研究[D].哈尔滨:东北林业大学,2014.Chen Qingwei.Quality Evaluation of Wood Surface Defects Image Segmentation based on Grey Theory[D].Harbin:Northeast Forestry University,2014.
[13]盛韩伟,戴培山,刘智航,等.基于拓扑结构的眼底图像分割评价新方法[J].生物医学工程学杂志,2015,32(5):1100-1105.Sheng Hanwei,Dai Peishan,Liu Zhihang,et al.New Approach of Image Segmentation Ecaluation Based on Topology Structure[J].Journal of Biomedical Engineering,2015,32(5):1100-1105.
[14]陈跃.钢板表面缺陷图像分割效果评价方法[J].制造业自动化,2015,37(18):66-68,85.Chen Yue.Segmentation Results Evaluation Method of Steel Plate Surface Defects Images[J].Manufacturing Automation,2015,37(18):66-68,85.
[15]蔡舒妤,师利中.飞机机体损伤区域的快速划分方法[J].计算机工程与设计,2016,37(3):737-741.CAI Shuyu,SHI Lizhong.Research On Airframe Damage Region Rapid Division Method[J].Computer Engineering and Design,2016,37(3):737-741.