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基于空间映射复Directionlet变换的图像纹理分类 被引量：1

Texture Classification Based on Mapping Complex Directionlet
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摘要 Directionlet变换具有多方向各向异性基函数,能有效捕捉图像的奇异性特征。该文在此基础上构造了一种空间映射的复Directionlet变换,使其具备了更为灵活的方向选择性和近似的平移不变性。利用空间映射方法获得Directionlet变换的复函数空间,对多尺度各方向子带系数提取能量特征用于图像纹理分类。通过对Brodatz图像库及真实SAR图像的纹理分类实验表明,该文算法较之小波分析及其它多尺度几何分析方法,具有更优的纹理分类性能,也验证了Directionlet工具在图像分析中的应用潜力。 Directionlet transform can capture the image singularity due to possessing the multi-direction anisotropic basis functions. A texture classification algorithm based on the Mapping complex Directionlet Transform （M-DT） is proposed, which provides better directionality and approximate shift invariance. By space mapping for the texture image, then complex Directionlet transform is applied to the mapped image, and the multiscale subband coefficient energy feature is used for texture classification. The experiments using texture images from Brodatz and real SAR images indicate the proposed method outperforms wavelets and Multiscale Geometric Analysis （MGA） approaches, the potential application to image analysis by Directionlet is thus proved.
作者 白静 贾建华 焦李成
机构地区 西安电子科技大学智能信息处理研究所和智能感知与图像理解教育部重点实验室
出处 《电子与信息学报》 EI CSCD 北大核心 2009年第6期1332-1336,共5页 Journal of Electronics & Information Technology
基金 国家863计划项目(2007AA12Z136) 国家973规划项目(2006CB705700) 国家自然科学基金(60672126) 国家教育部博士点基金(20050701013) 教育部长江学者和创新团队支持计划(IRT0645)资助课题
关键词 SAR图像 Brodatz图像 Directionlet变换 空间映射 纹理分类 SAR image Brodatz image Directionlet transform Space mapping Texture classification
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献13

  • 1Meyer F G and Coifman R R. Directional image compression with Brushlets[C]. Proceedings of the IEEE-SP International Symposium. Paris, France, June 1996: 18-21.
  • 2Cand~s E J. Ridgelets: theory and applications[D]. [Ph.D. dissertation]. Department of Statistics, Stanford University, 1998.
  • 3Candes E J and Donoho D L. Curvelets and curvilinear integrals[R]. Department of Statistics, Stanford University, CA, Tech. Rep., Dec. 1999.
  • 4Do M N and Vetterli M. The Contourlet transform: An efficient directional multiresolution image representation[J]. IEEE Trans. Image on Processing, 2005, 14(12): 2091-2106.
  • 5Velisavljevic V, Beferull-Lozano B, and Vetterli M, et al.. Directionlets: anisotropic multi-directional representation with separable filtering[J]. IEEE Trans. Image on Processing. 2006, 15(7): 1916-1933.
  • 6Velisavljevic V, Beferull-Lozano B, and Vetterli M, et al.. Low-rate reduced complexity image compression using Dircctionlets[C]. IEEE International Conference on Image Processing. (ICIP), Atlanta, GA, October 2006: 1601-1604.
  • 7Velisavljevic V, Beferull-Lozano B, and Vetterli M. Spacefrequency quantization for image compression with Directionlets [J]. IEEE Trans. Image on Processing, 2007, 16(7): 1761-1773.
  • 8Jobanputra R and Clausi D A. Preserving boundaries for image texture segmentation using grey level co-occurring probabilities[J]. Pattern Recognition, 2006, 39(2): 234-245.
  • 9Fernandes F C A, Van Spaendonck R L C, and Burrus C S. Multidimensional, mapping-based complex wavelet transforms[J]. IEEE Trans. Image on Processing, 2005, 14 (1) 110-124.
  • 10Fernandes F C A. Directional, shift-insensitive, complex wavelet transforms with controllable redundancy[D]. [Ph.D. dissertation]. Rice Univ. Houston, TX. 1, 2002.

同被引文献17

  • 1杨蕊红,潘泉,程咏梅.小波不变矩在图像识别中的应用研究[J].计算机应用研究,2005,22(11):239-240. 被引量：4
  • 2Manthalkar R, Briswas P K, Chatterji B N. Rotation and scale in- variant texture features using discrete wavelet packet transform[J]. Pattern Recognition Letters, 2003,24(14) : 2455 - 2462.
  • 3Charalampidis D, Kasparis T. Wavelet-based rotational rough- ness features for texture classification and segmentation [J]. IEEE Trans. on Image Processing, 2002,11 (8) : 825 - 837.
  • 4Crosier M, Griffin L D. Using basic image features for texture classifieation[J]. International Journal of Computer Vision, 2010,88(3) :446 -470.
  • 5Griffin L D, Lillholm M, Crosier M, et al. Basic image features (BIFs) arising from approximate symmetry type[C] //Proc. of the 2nd International Conference of Scale Space and Variation- al Methods in Computer Vision ,2009 : 343 - 355.
  • 6Crosier M, Griffin I. D. Texture classifycation with a dictionary of basic image features[C] // Proc. of the IEEE Conference on Computer Vision and Pattern Recognition ,2008:1 -7.
  • 7Griffin L D, Lillholm M. Feature category systems for 2nd order local image structure induced by natural image statistics and oth- erwise[C]//Proc, of the .SPIE, 2007, doi: 10. 1117/12. 714560.
  • 8Griffin L D, Lillholm M. Symmetry sensitivities of derivative- of Gaussian filters[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010,32(6) : 1072 - 1083.
  • 9Vapnik V N. Statistical learning theory [M]. New York: Wiley, 1998.
  • 10Christoper J C. A tutorial on support vector machines for pat- tern recognition[J]. Data Mining and Knowledge Discovery, 1998,2(2):121 - 167.

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电子与信息学报
2009年 第6期

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