Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (6): 1688-1691.DOI: 10.11772/j.issn.1001-9081.2016.06.1688

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Application of scale-invariant feature transform algorithm in image feature extraction

LIN Tao, HUANG Guorong, HAO Shunyi, SHEN Fei   

  1. Institute of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an Shaanxi 710038, China
  • Received:2015-11-06 Revised:2015-12-23 Online:2016-06-10 Published:2016-06-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61573373).


林陶, 黄国荣, 郝顺义, 沈飞   

  1. 空军工程大学 航空航天工程学院, 西安 710038
  • 通讯作者: 林陶
  • 作者简介:林陶(1991-),男,四川遂宁人,硕士研究生,主要研究方向:无人机视觉导航;黄国荣(1972-),男,陕西西安人,教授,博士,主要研究方向:导航制导与控制;郝顺义(1972-),男,山东临沂人,教授,博士,主要研究方向:导航制导与控制;沈飞(1991-),男,辽宁沈阳人,硕士研究生,主要研究方向:组合导航、联邦滤波。
  • 基金资助:

Abstract: The high complexity and long computing time of Scale-Invariant Feature Transform(SIFT) algorithm cannot meet the real-time requirements of stereo matching. And the mismatching rate is high when an image has many similar regions. To solve the problems, an improved stereo matching algorithm was proposed. The proposed algorithm was improved in two aspects. Firstly, because the circular has natural rotation invariance, the feature point was acted as the center and the rectangle region of the original algorithm was replaced by two approximate-size concentric circle regions in the improved algorithm. Meanwhile, the gradient accumulated values of 12 directions were calculated within the areas of the inner circle and the outer circle ring respectively, and the dimension of the local feature descriptor was reduced from 128 to 24. Then, a 12-dimensional global vector was added, so that the generated feature descriptor contained the SIFT vector based on local information and the global vector based on global information, which improved the resolving power of the algorithm when the images had similar areas. The simulation results show that, compared with the original algorithm, the real-time performance of the proposed algorithm was improved by 59.5% and the mismatching rate was decreased by 9 percentage points when the image had many similar regions. The proposed algorithm is suitable for in the case of high real-time image processing.

Key words: Scale-Invariant Feature Transform(SIFT) algorithm, stereo vision, feature point matching, global information, feature descriptor

摘要: 针对尺度不变特征转换(SIFT)算法复杂度高、计算时间长,难以满足立体匹配的实时性要求以及当图像中存在多个相似区域时误匹配率较高的问题,提出了一种改进的立体匹配算法。该算法从两个方面对SIFT算法进行了改进:首先,由于圆形具有天然的旋转不变性,该算法以特征点为中心,采用近似大小的两个同心圆区域代替原算法的矩形区域,在内圆和外圆环区域内分别统计12个方向的梯度累加值,把局部特征描述符的维数从128维降低到24维,降低了算法复杂度;其次加入了12维的全局向量,使生成的特征描述符包含了基于局部信息的SIFT向量和基于全局信息的全局向量,提高了算法对图像中相似区域的分辨能力。仿真结果表明,改进后的算法实时性比原算法提高了59.5%,当图像存在多个相似区域时,误匹配率下降了9个百分点。所提算法在图像处理的实时性要求较高的场合下适用性较好。

关键词: 尺度不变特征转换算法, 立体视觉, 特征点匹配, 全局信息, 特征描述符

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