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基于分形和神經(jīng)網(wǎng)絡(luò)理論的多尺度圖象分割方法

楊紹國,  尹忠科,  羅炳偉

楊紹國, 尹忠科, 羅炳偉. 基于分形和神經(jīng)網(wǎng)絡(luò)理論的多尺度圖象分割方法[J]. 電子與信息學報, 1998, 20(6): 727-732.
引用本文: 楊紹國, 尹忠科, 羅炳偉. 基于分形和神經(jīng)網(wǎng)絡(luò)理論的多尺度圖象分割方法[J]. 電子與信息學報, 1998, 20(6): 727-732.
Yang Shaoguo, Yin Zhongke, Luo Bingwei. MULTISCALE IMAGE SEGMENTATION USING FRACTAL AND NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1998, 20(6): 727-732.
Citation: Yang Shaoguo, Yin Zhongke, Luo Bingwei. MULTISCALE IMAGE SEGMENTATION USING FRACTAL AND NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1998, 20(6): 727-732.

基于分形和神經(jīng)網(wǎng)絡(luò)理論的多尺度圖象分割方法

MULTISCALE IMAGE SEGMENTATION USING FRACTAL AND NEURAL NETWORK

  • 摘要: 特征空間聚類分割方法存在的關(guān)鍵問題是有效的特征參數(shù)提取和聚類方法的構(gòu)造。針對這兩個問題,本文采用小波變換的多尺度分析方法提取圖象的多尺度分形維數(shù)作為分割特征參數(shù),用Kohonen自組織特征映射實現(xiàn)特征空間聚類,獲得了良好的分割效果。
    Abstract: Clustering algorithms in feature space are important methods in image segmentation. The choice of the effective feature parameters and the construction of the clustering method are key problems encountered with clustering algorithms. In this paper we chose multifractal dimensions as the segmentation feature parameters which are extracted from original image and wavelet-transformed image. SOM network is applied to cluster the segmentation feature parameters. The experiment shows that the performance of our algorithm is very good.
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  • Ishimura N. Estimation of fractal dimension values in natural navigation images and its application to region segmentation. Joural of Japan Institute of Navigation, 1994, 90(3): 43-51.[2]Fortin C S. Fractal dimension in the analysis of medical images. IEEE Engineering in Medicine and Biology Xlagazine, 1992, 11(，)65-71.[3]Chang J. Image segmentation (IS) and local fractal analyses of MR images. Conference Record of the 1992 IEEE Nuclear Science Symposium and Medical Imaging Conference, Orlando, FL, USA: 1992, 2: 1268-1273.[4]Lefebvre F. A fractal approach to the segmentation of microcalcifications in digital mammograms[J].Medical Physics.1995, 22(4):381-390[5]Wong S H. Automatic segmentation of ultrasonic image. Proceedings TENCON93, 1993 IEEE Region 10 Conference on `Computer, Communication, Control and Power Engineering, Beijing, China: 1993, Vo1.2, 910-913.[6]Chan K L. Quantitative characterization of electron micrograph image using fractal feature[J].IEEE Trans. on Biomedical Engineering.1995, 42(10):1033-1037[7]Moghaddam B. Ractal dimension segmentation of synthetic aperture radar imagery. ISSPA 92, Third International Symposium on Signal Processing and its Application, Proceedings, Gold Coast, Auslralia: 1992, 455-458[8]朱光喜, 張平, 朱煙庭. 基于分形維數(shù)的圖象分割研究.計算機科學, 1994, 21(1): 59-65.[9]羅立民,等.基于紋理分析的磁共振圖象區(qū)域分割.自動化學報,1995, 21(4): 504-508.[10]Xue Dong-hui. The object detection based on multiscale fractal character vector. IEEE International Conference on Neural Networks and Signal Processing, 1995, 1451-1454.[11]Pentland A P. Fractal-based description of natural scences. IEEE Trans. on Pattern Analysis[12]and Machine Intelligence, 1984, PAMI-6(6): 661-674.[13]Peli T. Multiscale fractal theory and object characterization[J].J. Opt. Soc. Am. A.1990, 7(6):1101-1112[14]Dubuisson M P. Efficacy of fractal features in segmenting images of natural textures[J].Pattern Recognition Letters.1994, 15(4):419-431[15]Kasparis T. Texture description using fractal and energy features[J].Computers Electrical Engineering.1995, 21(1):21-32[16]Chaudhuri B B. Texture segmentation using fractal dimension. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1995, PAMI-17(1): 72-77.[17]Mallat S G. A Theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1989, PAMI-11(7): 674-693.[18]Arneodo A. Wavelet transform of multifractals[J].Physical Review Letters.1988, 61(20):2281-2284[19]Xuegong Zhang. Self-organizing map as a new method for clustering and data analysis. Proceed-[20]ings of 1993 International Joint Conference on Neural Networks, Nagoya, Japan: 1993, 2448-2451.[21]Witoon Suewatanakul. Comparison of artificial neural networks and traditional classifiers via the two-spiral problem. SPIE, 1992, 1721: 275-282.
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  • 文章訪問數(shù):  2419
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出版歷程
  • 收稿日期:  1997-01-20
  • 修回日期:  1998-01-15
  • 刊出日期:  1998-11-19

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