A Comparison of Local Linear Feature Extraction

  • Hou Guo-qiang
  • Fu Xiao-ning
  • He Tian-xiang
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)


A number of studies have been carried out on the passive ranging method based on the monocular imaging features to non-cooperative target. This paper deals with target ranging estimation system focus on image linear feature. The ranging system implements target ranging by means of adjacent image matching method to extract target feature points, then obtain target rotational invariant linear feature, and combined target azimuth and pitching relative to camera when image is taken with camera space coordinate, the target distance to image pickup system is gained by solving the certain target ranging equation. As for target linear feature extraction, the paper applies three algorithms of the sub-pixel Harris corners method, the Simplified Scale Invariant Feature Transform (SSIFT) method and Speeded Up Robust features (SURF) method to extract linear feature and makes an analysis to ranging performance. It implied by our experiment that the SURF algorithm is the best one in the three methods. Its computational error is relatively small, and the time consumed is shorter compared with other two algorithms. The error of the sub-pixel Harris algorithm is a bit of larger than SSIFT algorithm while the real time realization performance is better than SSIFT algorithm.


Feature Descriptor Scale Space Target Distance Linear Feature Image Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fu, X.-N.: Research on Infrared Passive Location Technology from Mono-station. Doctor dissertation. Xidian University, Xi’an (2005)Google Scholar
  2. 2.
    Huang, S.-K., Xia, T., Zhang, T.-X.: Passive ranging method based on infrared images. Infrared and Laser Engineering 126(1), 109–112+126 (2007)Google Scholar
  3. 3.
    Guo, L., Xu, Y.-C., Li, K.-Q., Lian, X.-M.: Study on Real-time Distance Detection Based on Monocular Vision Technique. Journal of Image and Graphics 11(1), 74–81 (2006)Google Scholar
  4. 4.
    Wang, D., Fu, X.-N.: A passive ranging system based on image sequence from single lens and the imaging direction - Introduction and performance. In: ICNNT, Taiyuan, China (2011)Google Scholar
  5. 5.
    A method for distance estimation based on the imaging system. Chinese Patent (February 2010)Google Scholar
  6. 6.
    Liang, Z.-M., Gao, H.-M., Wang, Z.-J., Wu, L.: Sub-pixels corner detection for camera calibration. Transactions of the China Welding Institution 27(2), 102–104 (2006)Google Scholar
  7. 7.
    Zhao, W.-B., Zhang, Y.-N.: Survey on Corner Detecton. J. of Application Research of Computers 38(10), 17–19 (2006)Google Scholar
  8. 8.
    Zhang, S.-Z., Song, H.-L., Xiang, X.-Y., Zhao, Y.-N.: Fast SIFT Algorithm for Object Recognition. J. of Computer Systems & Applications 19(6), 82–85+186 (2010)Google Scholar
  9. 9.
    Liu, L., Peng, F.-Y., Zhao, K., Wan, Y.-P.: Simplified SIFT algorithm for fast image matching. J. of Infrared and Laser Engineering 37(1), 181–184 (2008)Google Scholar
  10. 10.
    Tang, Y.-H., Lu, H.-Z., Hou, W.-J.: Serial Images Matching Algorithm Based on DOG Feature Points. J. of Modern Electronics Technique 136(4), 128–130+136 (2008)Google Scholar
  11. 11.
    Lu, X.-M., Sun, Z.-J., Wu, J., Wang, J.-B., Yu, T.-X., Zhao, L., Ding, X.-H.: An Improved Algorithm of Image Registration based on SURF. Dunhuang Research (6), 88–92 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Xi’dian UniversityXi’anChina

Personalised recommendations