期刊文献+

基于复杂性指数的图像分割必要性判别技术 被引量:1

Discriminatory technology of necessity for image segmentation based on complexity index
下载PDF
导出
摘要 考虑到图像分割的复杂性实质上与图像中可分割的区域个数相关,而图像分割的必要性是与图像中可分割的区域大小有关,针对图像分割实际应用中部分图像的内容较少、无明显语义,不必进行图像分割的情况,提出一种基于图像内容语义、图像分割复杂性的图像分割必要性判别测度。进一步基于其测度定义,进行了大量相关实验,实验结果表明,基于复杂性指数的图像分割测度很好地完成了预期的功能,能够成为图像分割必要性有效合理的衡量依据。 Considering the complexity of image segmentation is essentially associated with the number of divisible area of image, and the necessity for image segmentation is related to the size of the dividable region in the image, in view of some of the image contents are less and lack of clear semantics, even unnecessary for segmentation in the practical application of image segmentation, an image segmentation necessity discriminatory measures based on image content semantics and the complexity of image seg- mentation is proposed. A lot of experiments based on the measure definition are carried out. Experimental results show that the proposed image segmentation based on complexity index measure well implements the expected function. The image segmenta- tion based on complexity index measure can be the effective and reasonable measure for the image segmentation necessity.
出处 《计算机工程与应用》 CSCD 2013年第16期155-157,200,共4页 Computer Engineering and Applications
基金 国家自然科学基金(No.61202285) 河南省科技厅科学技术重点项目(No.12A510001)
关键词 图像分割 复杂性 测度 image division complexity evaluation index
  • 相关文献

参考文献10

  • 1Chen C W, Luo J, Parker K J.Image segmentation via adaptive K-mean clustering and knowledge-based morphological oper- ations with biomedical applications[J].IEEE Transactions on Image Processing, 1998,7(12) : 1673-1683.
  • 2Yang X D, Gupta V.An improved threshold selection method for image segmentation[C]//Proceedings of 1993 IEEE Cana- dian Conference on Electrical and Computer Engineering. Waterloo, Canada: IEEE Computer Society, 1993,1 : 531-534.
  • 3Shi Jianbo, Malik J.Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2000,22 (8) : 888-905.
  • 4Comaniciu D,Meer P.Mean shift:a robust approach toward feature space analysis[J].IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 2002,24 ( 5 ) : 603-619.
  • 5Li Chunming,Xu Chenyang,Gui Changfeng, et al.Level set evolution without re-initialization: a new variational formula- tion[C]//Proceedings of 2005 IEEE Computer Society Con-ference on Computer Vision and Pattern Recognition.Piscat- away,N J, USA: [EEE Computer Society, 2005,1 : 430-436.
  • 6Dougherty E R.Mathematical morphology in image process- ing[M].New York: M Dekker, 1993 : 87-91.
  • 7Felzenszwalb P F, Huttenlocher D P.Efficient graph-based image segmentation[J].International Journal of Computer Vision, 2004,59(2) : 167-181.
  • 8Larrabide I, Feij R A, Taroco E, et al.Configurational derivative as a tool for image segmentation[C]//Proceedings of 2006 the 3rd European Conference on Computational Mechanics Solids, Structures and Coupled Problems in Engineering.Lisbon, Portugal: LNEC, 2006 : 1-11.
  • 9Montoya MDG, Gil C, Garcia I.Load balancing for a class of irregular and dynamic problems: region growing image seg- mentation algorithms[C]//Proceedings of the 1999 Seventh Euromicro Workshop on Parallel and Distributed Processing. Funchail, Portugal : IEEE Comtmter Society. 1999 : 163-169.
  • 10Zavaljevski A, Dhawan AP, Gaskil M, et al.MultiAevel adaptive segmentation of multi-parameter MR brain images[J].Comput- erized Medical Imaging and Graph,2000,24(2):87-98.

同被引文献3

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部