航空学报 > 2012, Vol. 33 Issue (12): 2313-2321

基于改进SIFT的高鲁棒性特征点提取方法

邰能建, 吴德伟, 戚君宜   

  1. 空军工程大学 信息与导航学院, 陕西 西安 710077
  • 收稿日期:2011-12-30 修回日期:2012-02-09 出版日期:2012-12-25 发布日期:2012-12-24
  • 通讯作者: 吴德伟 E-mail:wudewei74609@126.com
  • 作者简介:邰能建 [JP2]男, 博士研究生。主要研究方向: 无人机智能自主导航。 E-mail: tainengjian@yahoo.com.cn
  • 基金资助:

    国家自然科学基金(61273048,61001111);国防科技重点实验室资助项目(9140C0201010902)

A Method to Extract High Robust Keypoints Based on Improved SIFT

TAI Nengjian, WU Dewei, QI Junyi   

  1. School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
  • Received:2011-12-30 Revised:2012-02-09 Online:2012-12-25 Published:2012-12-24
  • Supported by:

    National Natural Science Foundation of China (61273048, 61001111); National Defense Science and Technology Key Laboratory Funded Item (9140C0201010902)

摘要:

为实现无人作战飞机(UCAV)认知导航的高鲁棒性特征点提取,提出一种基于自适应非极大值抑制(ANMS)的多元量化Hessian-Affine迭代式尺度不变特征变换(SIFT)方法。针对认知导航对特征点均匀分布的需求,提出基于ANMS的初始特征点优选算法。为确保特征点的仿射不变性,利用引入迭代调节因子的Hessian-Affine迭代算法估计仿射不变区域,并在对应归一化圆形区域进行主方向确定以及圆形描述子生成。针对模拟特征序列分布不均匀、正确匹配率不高的缺陷,采用多值量化与比特抽取结合法对模拟特征序列进行多元量化,并且分析验证了该方法的优越性能。仿真结果表明,本文方法具有较高的正确匹配率,具有旋转不变性和尺度不变性,其抗噪性能提高了10 dB,并且在大视角变化范围内具有较优的抗仿射性能。

关键词: 认知导航, 迭代SIFT, 仿射不变, 量化, 自适应非极大值抑制

Abstract:

In order to extract high robust keypoints for the cognitive navigation of unmanned combat aerial vehicle (UCAV), a method named multi-quantifying Hessian-Affine iterative scale invariant feature transform (SIFT) with adaptive non-maximum suppression (ANMS) is proposed. Considering the demand of well-distributed keypoints, an optimization arithmetic based on ANMS is first presented to choose the keypoints. In order to ensure the chosen keypoints’ affine invariant property, a Hessian-Affine iterative arithmetic with an iterative adjusting factor is used to estimate the affine invariant regions, and then the main orientation assignment and cycle descriptor are further realized in the corresponding normalized cycle regions. In view of the deficiencies of analog feature vectors in balanced distribution and correct matching score, a method combining the multiple value quantization and reshaping operation is presented to quantify the analog feature vectors. The analysis and simulation results verify this quantifying method as having better properties. Simulation results prove that the method proposed in this paper has higher correct matching score. It is invariant to image rotation and scaling. It can improve the anti-noise property by over 10 dB, and it possesses robust affine invariant property within a large visual angle range.

Key words: cognitive navigation, iterative SIFT, affine invariant, quantization, adaptive non-maximum suppression

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