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基于分數(shù)布朗運動和概率神經(jīng)網(wǎng)絡(luò)的自然紋理分類

胡金艷,  張?zhí)?/a>,  張春梅

胡金艷, 張?zhí)? 張春梅. 基于分數(shù)布朗運動和概率神經(jīng)網(wǎng)絡(luò)的自然紋理分類[J]. 電子與信息學(xué)報, 2004, 26(3): 389-393.
引用本文: 胡金艷, 張?zhí)? 張春梅. 基于分數(shù)布朗運動和概率神經(jīng)網(wǎng)絡(luò)的自然紋理分類[J]. 電子與信息學(xué)報, 2004, 26(3): 389-393.
Hu Jin-yan, Zhang Tai-yi, Zhang Chun-mei. Texture Classification Using Fractional Brownian Motion and Probabilistic Neural Network[J]. Journal of Electronics & Information Technology, 2004, 26(3): 389-393.
Citation: Hu Jin-yan, Zhang Tai-yi, Zhang Chun-mei. Texture Classification Using Fractional Brownian Motion and Probabilistic Neural Network[J]. Journal of Electronics & Information Technology, 2004, 26(3): 389-393.

基于分數(shù)布朗運動和概率神經(jīng)網(wǎng)絡(luò)的自然紋理分類

Texture Classification Using Fractional Brownian Motion and Probabilistic Neural Network

  • 摘要: 該文基于自然紋理的分數(shù)布朗運動(FBM)模型，引入一種新的分形特征參數(shù)分形尺度，提出計算分形尺度的截矩線性度(IAL)方法。實驗表明，將紋理圖像的分形尺度與分形維數(shù)結(jié)合，既反映了物理表面分形的尺度范圍又反映了該尺度范圍內(nèi)的紋理粗糙程度，從而更有效地描述和區(qū)分紋理特征。文中利用紋理圖像在水平、垂直和對角3個方向上的分形尺度和分形維數(shù)作為特征集，采用概率神經(jīng)網(wǎng)絡(luò)(PNN)作為分類器進行自然紋理圖像分類，得到準確的分類結(jié)果。
    Abstract: In this paper, the fractal scale is introduced as a new measure to describe natural physical surfaces. An Intercept And Linearity (IAL) method is proposed to determine the fractal scale automatically. Experiments indicate that the fractal scale, together with the fractal dimension can effectively reflect the fractal scale range of natural textures and the texture roughness within the scale range. For the purpose of classification, the fractal scales and the fractal dimensions along horizontal, vertical and diagonal directions are obtained to form a feature set with six parameters. The probabilistic neural network is used as the texture classifier. Results have shown satisfied classification of natural textures.
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  • Mandelbrot B B, Van Ness J W. Fractional Brownian motion, fractional noise and applications[J].SIAM Rev.1968, 10(4):422-437[2]Pentland A P. Fractal-based description of natural scenes. IEEE Trans. on Pattern Anal. Machine Intell., 1984, PAMI-6(11): 666-674.[3]Liu S, Chang S. Dimension estimation of discrete-time fractional Brownian motion with applications to image texture classification. IEEE Trans. on Image Processing, 1997, IP-6(8): 1176-1184.[4]Stewart C V, Moghaddam B, Hintzm K J, et al.. Fractional Brownian motion models for synthetic aperture radar imagery scene segmentation[J].Proc. IEEE.1993, 81(10):1511-1522[5]Potlapalli H, Luo R C. Fractal-based classification of natural textures[J].IEEE Trans. on Industrial Electronics.1998, 45(2):142-150[6]Chen C C, Daponte J S, Fox M D. Fractal feature analysis and classification in medical imaging.IEEE Trans. Med. Imag., 1989, MI-8(6): 133-142.[7]Chen E L, Chung P C, Chen C L, et al.. An automatic diagnostic system for CT liver image classification[J].IEEE Trans. on Biomedical Engineering.1998, 45(6):783-794[8]Wu C M, Chen Y C, Hsieh K S. Texture feature for classification of ultrasonic liver images. IEEE Trans. Med. Imag., 1992, MI-11(4): 141-152.[9]Keller J M, Chen S. Texture description and segmentation through fractal geometry[J].Computer Vision, Graphics and Image Processing.1989, 45(2):150-166[10]Kaplan L M, Kuo J C. Texture roughness analysis and synthesis via extended self-similar (ESS)model. IEEE Trans. on Pattern Anal. Machine Intell., 1995, PAMI-17(11): 1043-1056.[11]Yokoya N, Yamamoto K. Fractal-based analysis and interpolation of 3D natural surface shapes and their application to terrian modeling[J].Computer Vision, Graphics and Image Processing.1989, 46(6):284-302[12]Brodatz P. Textures: A Photographic Album for Artists and Designers, New York: Dover, 1966:9-112.
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出版歷程
  • 收稿日期:  2002-11-23
  • 修回日期:  2003-04-07
  • 刊出日期:  2004-03-19

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