计算机科学 ›› 2010, Vol. 37 ›› Issue (11): 275-277.

• 图形图像 • 上一篇    下一篇

Contourlet域目标不变特征提取

梅雪,夏良正   

  1. (南京工业大学自动化与电气工程学院 南京210009;(东南大学自动化学院 南京210096)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(60805002),江苏省高校自然科学基金项目((09KJB570002),江苏省高新技术重点实验室项目(BM200707 ),南京工业大学学术基金项目(39710006)资助。

Object Invariant Feature Extraction in Contourlet Field

MEI Xue,XIA Liang-zheng   

  • Online:2018-12-01 Published:2018-12-01

摘要: 在基于形状的目标识别中,提取出鉴别力强并具有不变性的特征是至关重要的问题。多尺度几何分析具有多方向选择性和各向异性的特点,能够更有效地表示目标图像的局部特征,但这些变换本身不具备不变性,极大地限制了它在模式识别中的应用。利用图像广义矩的概念,提出了一种在Contourlet域具有平移、缩放及旋转不变性特征的描述子,该特征能精细地刻画目标区域的局部特性,并在位置、角度及尺寸变换情况下具有不变性,仿真实验验证了其不变性,并讨论了一般情况下,Contourlct变换分解尺度对不同类目标间分离度的影响,为提取最具鉴别性的特征提供了有益的参考。

关键词: 特征提取,多尺度几何分析,Contourlet变换,不变性特征

Abstract: Extracting features which arc invariant and can discriminate targets with similar shape is one of key problems in shape-based target recognition. Multiscale geometric analysis(MUA) offers a high degree of directionality and anisotropy,which can express local features of objects more effectively. However, it is restricted greatly when used for object recognition because most of Multiscale geometric transforms are not invariant. A new feature descriptor which is invariant to the translation, scaling and rotation, was constructed in Contourlet field in this paper, which uses the idea of the image generalized moment. This method is specialized in extracting target local characters. Experimental results demonstrate the potential of Contourlet in feature extraction, and the features will not vary with translations, scaling and rotation. Furthermore, a study of the influence of using different decompose scale of Contourlet was carried out.

Key words: Feature extraction, MUA, Contourlet transform, Invariant feature

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