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一种基于Local Jet结构的全局图像特征构造方法

谢锦 蔡自兴

谢锦, 蔡自兴. 一种基于Local Jet结构的全局图像特征构造方法. 自动化学报, 2014, 40(6): 1148-1155. doi: 10.3724/SP.J.1004.2014.01148
引用本文: 谢锦, 蔡自兴. 一种基于Local Jet结构的全局图像特征构造方法. 自动化学报, 2014, 40(6): 1148-1155. doi: 10.3724/SP.J.1004.2014.01148
XIE Jin, CAI Zi-Xing. A Global Image Feature Construction Method Based on Local Jet Structure. ACTA AUTOMATICA SINICA, 2014, 40(6): 1148-1155. doi: 10.3724/SP.J.1004.2014.01148
Citation: XIE Jin, CAI Zi-Xing. A Global Image Feature Construction Method Based on Local Jet Structure. ACTA AUTOMATICA SINICA, 2014, 40(6): 1148-1155. doi: 10.3724/SP.J.1004.2014.01148

一种基于Local Jet结构的全局图像特征构造方法

doi: 10.3724/SP.J.1004.2014.01148

A Global Image Feature Construction Method Based on Local Jet Structure

Funds: 

Supported by National Natural Science Foundation of China(90820302)and Scientific Research Fund of Hunan Provincial Education Department(12C0202)

More Information
    Corresponding author: CAI Zi-Xing
  • 摘要: 提出一种鲁棒的特征描述符MSALJS (Multi-Scale Autoconvolution on Local Jet Structure),该描述符对仿射变换具有近似不变性. MSALJS是一种全局图像特征描述符,它基于描述图像局部结构的微分进行多尺度自卷积矩计算. 实验结果表明,MSALJS能适用于目标识别实际应用时图像发生部分遮挡、视角变化等变形情形.
  • [1] Hu M. Visual pattern recognition by moment invariants. IEEE Transactions on Information Theory, 1962, 8(2): 179-187
    [2] [2] Farokhi S, Shamsuddin S M, Flusser J, Sheikh U U, Khansari M, Jafari-Khouzani K. Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis. Journal of Electronic Imaging, 2013, 22(1): 1-11
    [3] [3] Flusser J. Pattern recognition by affine moment invariants. Pattern Recognition, 1993, 26(1): 167-174
    [4] [4] Tomas S, Flusser J. Affine moment invariants generated by graph method. Pattern Recognition, 2011, 44(9): 2047-2056
    [5] [5] Tomas S, Flusser J. Combined blur and affine moment invariants and their use in pattern recognition. Pattern Recognition, 2003, 36(12): 2895-2907
    [6] [6] Rahtu E, Salo M, Heikkila J. Affine invariant pattern recognition using multi-scale autoconvolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(6): 908-918
    [7] [7] Lindeberg T. Scale selection properties of generalized scale-space interest point detectors. Journal of Mathematical Imaging and Vision, 2013, 46(2): 177-210
    [8] [8] Baumberg A. Reliable feature matching across widely separated views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, South Carolina, USA: IEEE, 2000. 774-781
    [9] [9] Mikolajczyk K, Schmid C. Scale affine invariant interest point detectors. International Journal of Computer Vision, 2004, 60(1): 63-86
    [10] Lan R, Yang Jian-Wei, Jiang Yong, Song Zhan, Tang Yuan-Yan. An affine invariant discriminate analysis with canonical correlation analysis. Neurocomputing, 2012, 86(1): 184-192
    [11] Morel JM, Yu Guo-Shen. ASIFT: a new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009, 2(2): 438-469
    [12] Cui Chun-Hui, Ngan K N. Scale-and affine-invariant fan feature. IEEE Transactions on Image Processing, 2011, 20(6): 1627-1640
    [13] Ferraz L, Binefa X. A sparse curvature-based detector of affine invariant blobs. Journal of Computer Vision and Image Understanding, 2012, 116(4): 524-537
    [14] Koenderink J J. The structure of images. Biological Cybernetics, 1984, 50(5): 363-370
    [15] Koenderink J J, van Doorn A J. Representation of local geometry in the visual system. Biological Cybernetics, 1987, 55(6): 367-375
    [16] Florack L M J, Romeny B M, Koenderink J J, Viergever M. General intensity transformations and differential invariants. Journal of Mathematical Imaging and Vision, 1994, 4(2): 171-187
    [17] Koenderink J J, van Doorn A. The structure of visual spaces. Journal of Math Imaging Vision, 2008, 31(2-3): 171-187
    [18] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
    [19] Kadyrov A, Petrou M. The trace transform and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(8): 811-828
    [20] Geusebroek J M, Burghouts G J, Smeulders A W M. The Amsterdam library of object images. International Journal of Computer Vision, 2005, 61(1): 103-112
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出版历程
  • 收稿日期:  2011-09-02
  • 修回日期:  2013-08-23
  • 刊出日期:  2014-06-20

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