Advertisement

Object Classification Using SIFT Algorithm and Transformation Techniques

  • T. R. Vijaya Lakshmi
  • Ch. Venkata Krishna Reddy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

Recognition of objects, as well as identification and localization of three dimensional environments is a part of computer vision. In the proposed study the objects in a war field are classified. Images extracted from the video stream are utilized to classify the objects of interest (soldier, tree and tank). Distinguishable features of the objects are extracted and these features are used to identify and classify the objects. The SIFT algorithm used to find the features from such images are processed to classify the objects such as soldier, tank, tree, etc. The key points generated using SIFT algorithm are used to build a pyramid. The features extracted from these pyramids using various transforms are further classified in this work.

Keywords

Object identification SIFT key points Transformation techniques 

References

  1. 1.
    Stein, F., Medioni, G.: Structural indexing: efficient 3-D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 125–145 (1992)CrossRefGoogle Scholar
  2. 2.
    Koley, C., Midya, B.L.: 3-D object recognition system using ultrasound. In: Proceedings of the 3rd International Conference on Intelligent Sensing and Information Processing, Bangalore, pp. 99–104 (2005)Google Scholar
  3. 3.
    Mashor, M.Y., Osman, M.K., Arshad, M.R.: 3D object recognition using 2D moments and HMLP network. In: Proceedings in International Conference on Computer Graphics, Imaging and Visualization, pp. 126–130 (2004)Google Scholar
  4. 4.
    Kim, W.Y., Kak, A.C.: 3-D object recognition using bipartite matching embedded in discrete relaxation. IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 224–251 (1991)CrossRefGoogle Scholar
  5. 5.
    Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J.: 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2270–2287 (2014)CrossRefGoogle Scholar
  6. 6.
    Lowe, D.G.: Local feature view clustering for 3D object recognition. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 682–688 (2001)Google Scholar
  7. 7.
    Reddy, A.D., et al.: Quantifying soil carbon loss and uncertainty from a peatland Wild reusing multi-temporal LiDAR. Remote Sens. Environ. 170, 306–316 (2015)CrossRefGoogle Scholar
  8. 8.
    Kodors, S., et al.: Building recognition using LiDAR and energy minimization approach. Procedia Comput. Sci. 43, 109–117 (2015)Google Scholar
  9. 9.
    Lam, J., Greenspan, M.: 3D object recognition by surface registration of interest segments. In: International Conference On 3D Vision, pp. 199–206 (2005)Google Scholar
  10. 10.
    Flynn, P.I., Jain, A.K.: 3-D object recognition using constrained search. IEEE Trans. Pattern Anal. Mach. Intell. 13(10) (1991)Google Scholar
  11. 11.
    Dumont, G., Berthiaume, F., St-Laurent, L., Debaque, B., Prevost, D.: AWARE: a video monitoring library applied to the air traffic control context. In: International Conference on Advanced Video and Signal Based Surveillance, pp. 153–158 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • T. R. Vijaya Lakshmi
    • 1
  • Ch. Venkata Krishna Reddy
    • 2
  1. 1.MGITGandipetIndia
  2. 2.CBITGandipetIndia

Personalised recommendations