采用改进尺度不变特征变换在多变背景下实现快速目标识别

聂海涛; 龙科慧; 马军; 刘金国

doi:10.3788/OPE.20152308.2349

您当前的位置：

首页 >

文章列表页 >

采用改进尺度不变特征变换在多变背景下实现快速目标识别

信息科学 | 更新时间：2020-08-12

- 采用改进尺度不变特征变换在多变背景下实现快速目标识别
- Fast object recognition under multiple varying background using improved SIFT method
- 光学精密工程 2015年23卷第8期页码：2349-2356
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,100039
- 作者简介：
- 基金信息：
  
  中国科学院长春光学精密机械与物理研究所创新基金资助项目(No.Y2CX1SS125)()
- DOI：10.3788/OPE.20152308.2349
  中图分类号： TP391.4
- 收稿日期：2014-09-22，
  
  修回日期：2014-10-22，
  
  纸质出版日期：2015-08-25
- 稿件说明：
移动端阅览
聂海涛, 龙科慧, 马军等. 采用改进尺度不变特征变换在多变背景下实现快速目标识别[J]. 光学精密工程, 2015,23(8): 2349-2356

NIE Hai-tao, LONG Ke-hui, MA Jun etc. Fast object recognition under multiple varying background using improved SIFT method[J]. Editorial Office of Optics and Precision Engineering, 2015,23(8): 2349-2356
聂海涛, 龙科慧, 马军等. 采用改进尺度不变特征变换在多变背景下实现快速目标识别[J]. 光学精密工程, 2015,23(8): 2349-2356 DOI： 10.3788/OPE.20152308.2349.

NIE Hai-tao, LONG Ke-hui, MA Jun etc. Fast object recognition under multiple varying background using improved SIFT method[J]. Editorial Office of Optics and Precision Engineering, 2015,23(8): 2349-2356 DOI： 10.3788/OPE.20152308.2349.

摘要

提出一种改进的尺度不变特征变换(SIFT)算法

用于实现多变背景下的快速目标识别。首先

构建目标图像尺度空间

提取SIFT特征点并将其按大小分类

目标识别时只需比较同一类型的特征点。然后

由SIFT特征点子区域方向直方图计算得到4个新角度用于代表特征点的方向信息

并且在目标识别时根据角度信息限制特征点匹配范围

从而提高SIFT算法的运算速度。最后

计算目标图像和待识别图像之间的尺度因子

在尺度因子约束条件下进行目标特征点匹配

从而有效地保证正确匹配数量

提高目标识别的鲁棒性。实验结果表明:当目标在待识别图像中发生局部遮挡、旋转、尺度变化或者弱光照等情况下

改进的SIFT算法能够完成多变背景下快速目标识别任务

平均识别速度提升了40%。

Abstract

An improved Scale Invariant Feature Transform (SIFT) method was proposed to implement the fast object recognition under a multiple varying background. Firstly

the scale space of object image was established

SIFT feature points were extracted and classified by their sizes. Only by comparing the same kinds of feature points

the target recognition could be completed. Then

four new angles were computed from the sub-region orientation histogram to represent the orientation information of each SIFT feature. Meanwhile

the feature point matching range was limited according to angle information in the target recognition to improve the calculation speeds of the SIFT algorithm. Finally

the scale factor between object image and target image was calculated and the object feature points were matched under the constraint by the scale factor to increase the number of correct matches and to insure the robustness of object recognition. Object recognition experiments were operated under object external occlusions

object rotation

scale change and illumination conditions. Results show that improved SIFT method has better performance of object recognition

and its computation speed has raised more than 40% as comparing with that of original SIFT algorithm.

关键词

Keywords

references

LOWE D. Distinctive image features from scale-invariant keypoints, cascade filtering approach [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

YANG J C, YU K, GONG Y H, et al.. Linear spatial pyramid matching using sparse coding for image classification [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2009, 1794-1801.

ROSTEN E, PORTER R, DRUMMOND T. Faster and better: a machine learning approach to corner detection [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2010, 32(1): 105-119.

贾平,徐宁,张叶. 基于局部特征提取的目标自动识别[J]. 光学精密工程,2013,21(7):1898-1905. JIA P,XU N,ZHANG Y. Automatic target recognition based on local feature extraction [J]. Opt. Precision Eng.,2013, 21(7):1898-1905.(in Chinese)

赵立荣,朱玮,曹永刚,等. 改进的加速鲁棒特征算法在特征匹配中的应用 [J]. 光学精密工程,2013,21(12):3263-3271. ZHAO L R, ZHU W, CAO Y G, et al.. Application of improved SURF algorithm to feature matching [J]. Opt. Precision Eng.,2013,21(12):3263-3271.(in Chinese)

KE Y, SUKTHANKAR R. PCA-SIFT: a more distinctive representation for local image descriptor[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2004,2:506-513.

纪华,吴元昊,孙宏海,等. 结合全局信息的SIFT特征匹配算法[J]. 光学精密工程,2009,17(2):439-444. JI H, WU Y H, SUN H H, et al.. SIFT feature matching algorithm with global information [J]. Opt. Precision Eng., 2009, 17(2):439-444.(in Chinese)

CHARIOT A, KERIVEN R. GPU-boosted online image matching[C]. in Proceeding of the 19th Conference on Pattern Recognition, 2008, 1-4.

SILPA-ANAN C, HARTLEY R. Optimised KD-trees for fast image descriptor matching[C]. in Proc. IEEE International Conference on Computer Vision and Pattern Recognition, 2008, 1-8.

FARAJ A, DANIJELA R D, AXEL G. Speeded up image matching using split and extended SIFT features [C]. Proceedings of the 5th International Conference on Computer Vision Theory and Applications (VISAPP 2010), 2010, 287-295.

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection [C]. IEEE Computer Society Conference on Computer Society, CVPR 2005, 886-893.

SABDI A, HASHEMI H, NADER E S. On the PDF of the sum of random vectors [J]. IEEE Transactions on Communications, 2000, 48(1): 7-12.

Assistantrobot FRIENDIII[EB/OL].[2014-01-16] http://www.iat.uni-bremen.de/sixcms/detail.php?id=555

XU W T, HUNG Y S, NIRANJAN M, et al.. Asymptotic mean and variance of gini correlation for bivariate normal samples [J]. IEEE Transaction on Signal Processing, 2010, 58(2): 522-534.

NEUMANN L, MATAS J. Text localization in real-world images using efficiently pruned exhaustive search [C]. 2011 International Conference on Document Analysis and Recognition, ICDAR, 2011, 687-691.

CHUM O, MATAS J. Optimal randomized RAN-SAC [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2008, 30(8):1472-1482.

浏览量

417

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

弱纹理环境下融合点线特征的双目视觉同步定位与建图

针对目标遮挡的自适应特征匹配网络

基于自适应惯导辅助特征匹配的视觉SLAM算法

融合二维图像和三维点云的相机位姿估计

采用深度级联卷积神经网络的三维点云识别与分割