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一種基于投影的小波矩及其在圖像識(shí)別中的應(yīng)用

崔培玲,  潘泉,  李軍宏,  張洪才,  程詠梅

崔培玲, 潘泉, 李軍宏, 張洪才, 程詠梅. 一種基于投影的小波矩及其在圖像識(shí)別中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 2005, 27(11): 1774-1777.
引用本文: 崔培玲, 潘泉, 李軍宏, 張洪才, 程詠梅. 一種基于投影的小波矩及其在圖像識(shí)別中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 2005, 27(11): 1774-1777.
Cui Pei-ling, Pan Quan, Li Jun-hong, Zhang Hong-cai, Cheng Yong-mei. Image Recognition Using a Projection-Based Wavelet Moment[J]. Journal of Electronics & Information Technology, 2005, 27(11): 1774-1777.
Citation: Cui Pei-ling, Pan Quan, Li Jun-hong, Zhang Hong-cai, Cheng Yong-mei. Image Recognition Using a Projection-Based Wavelet Moment[J]. Journal of Electronics & Information Technology, 2005, 27(11): 1774-1777.

一種基于投影的小波矩及其在圖像識(shí)別中的應(yīng)用

Image Recognition Using a Projection-Based Wavelet Moment

  • 摘要: 該文提出了一種基于圖像投影的小波不變量算法。首先將圖像投影到一維空間，然后引入一種平移和比例不變自適應(yīng)一維小波變換，利用該變換獲得圖像的平移和比例不變特征。由于圖像的旋轉(zhuǎn)導(dǎo)致特征矩陣發(fā)生行之間的循環(huán)平移，該文利用奇異值分解進(jìn)行研究，得到的奇異值向量具有圖像的3個(gè)不變性。實(shí)驗(yàn)分析表明，本文方法具有較好的圖像識(shí)別效果。
    Abstract: In this paper, a new wavelet invariant moment based on image projections is proposed. Firstly, Radon transform is used to project the image to 1-D space. Then, a translation and scaling invariant adaptive 1-D wavelet transform is introduced, and the projection data is transformed to wavelet domain to get a feature matrix with translation and scaling invariance. Since the feature matrix is influenced to be cyclically shifted of rows when image rotates, Singular Value Decomposition (SVD) is employed and singular value vector is invariant under image translation, scaling and rotation. Simulation results are given to obtain insight into the efficiencies offered by this method.
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  • Hu M K.Visual pattern recognition by moment invariant. IRE Trans. on Information Theory, 1962, 1(8): 179-187.[2]潘泉, 程詠梅, 杜亞娟, 張洪才. 離散不變矩算法極其在目標(biāo)識(shí)別中的應(yīng)用[J].電子與信息學(xué)報(bào).2001, 23(1):30-36瀏覽[3]Lo C H, Don H S. Pattern recognition using 3-D moments. 10th International Conference on Pattern Recognition, Atlantic City, USA, 1990, 1: 540-544.[4]Khotanzad A, Hong Y H. Invariant image recognition by Zernike moments[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1990, 12(5):489-497[5]Liao S X, Pawlak M. On image analysis by moments[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1996, 18(3):254-266[6]The C H, Chin R T. On image analysis by the methods of[7]moments. IEEE Trans[J].on Pattern Analysis and Machine Intelligence.1988, 10(4):496-513[8]Shen D G, Ip H. Discriminative wavelet shape descriptors for recognition of 2-D patterns[J].Pattern Recognition.1999, 32(2):151-165[9]Xiong H L, Zhang T X, Moon Y S. A translation- and scale- invariant adaptive wavelet transform[J].IEEE Trans. on Image Processing.2000, 9(12):2100-2108[10]Pun C M, Lee M C. Log-polar wavelet energy signatures for rotation and scale invariant texture classification[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2003, 25(5):590-603[11]Tian J. Comments on A translation- and scale-invariant adaptive wavelet transform[J].IEEE Trans. on Image Processing.2003, 12(9):1091-1093[12]Deans S R. The Radon transform and some of its applications. New York, Wiley, 1983,: 10.[13]Li J H, Pan Q, Zhang H C, Cui P L. Image recognition using Radon transform. The IEEE 6th International Conference on Intelligent Transportation Systems-IV: Image Analysis, Shanghai, China, 2003, 4: 741-745.[14]Al-Shaykh O K, Doherty J F. Invariant image analysis based on Radon transform and SVD. IEEE Trans. on Circuits and Systems-II: Analysis and Signal Processing, 1996, 43(2): 123-133.[15]ftp://ftp.cfar.umd.edu/pub/documents/contrib/databases/UMDlogo_database.tar
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  • 收稿日期:  2004-05-12
  • 修回日期:  2005-05-18
  • 刊出日期:  2005-11-19

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