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Object Detection Using Point Feature Matching Based on SURF Algorithm

  • Monika DeyEmail author
  • Ashim Saha
  • Anurag De
Conference paper
  • 75 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1192)

Abstract

Object detection addresses detecting instances of objects of a particular category in digitized images and videos. Every class containing the object is associated with special features that assist in classifying the class to which it belongs. The algorithm used can detect local features and extract them for the concerned object, known as descriptor points [1]. Then compares those extracted features or descriptors with the presumed features of the original image. Matching process among the original image and the target image yields similar features, based on which a decision is made. The mentioned algorithm is called Speeded up Robust Features (SURF) algorithm. The algorithm is based on Scale-invariant feature transform (SIFT) algorithm. The implementation procedure of SURF algorithm along with experiment and its results are stated in the paper. Also, the accuracy of detection of objects using point feature matching methodology has been calculated by means of sensitivity and specificity parameters.

Keywords

Object detection Descriptor points Point feature matching SURF algorithm 

References

  1. 1.
    Yang, Z.L., Guo, B.L.: Image mosaic based on SIFT. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1422–1425 (2008)Google Scholar
  2. 2.
    Bodke, V.S., Vaidya, O.S.: Object recognition in a cluttered scene using point feature matching. IJRASET 5(IX), 286–290 (2017) Google Scholar
  3. 3.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006).  https://doi.org/10.1007/11744023_32 CrossRefGoogle Scholar
  4. 4.
    S. U. R. F. (SURF): Herbet bay, andreas ess, tinne tuytelaars, luc van gool. Elsevier pre-print (2008)Google Scholar
  5. 5.
    Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: IEEE CVPR (2001) Google Scholar
  6. 6.
    Todkari, V., Pawar, A., Shanbhag, Y., Rajput, G., Bhoye, T.S.: A survey on suspicious object detection. IJARIIE 4(1), 628–631 (2018). ISSN(O)-2395-4396Google Scholar
  7. 7.
    Lowe, D.: Distinctive image features from scale-invariant key point. Int. J. Comput. Vis. 60, 1–28 (2004)CrossRefGoogle Scholar
  8. 8.
    Rasool Reddy, K., Krishna, K.V.S., Ravi Kumar, V.: Detection of objects in cluttered scenes using matching technique. IJECT 5(spl – 3), 42–44 (2014) Google Scholar
  9. 9.
    Hemalatha, S., Santhosh Kumar, S.: Image forgery detection using key-point extraction and segmentation. IJPT 8, 13219–13229 (2016)Google Scholar
  10. 10.
    Saleem, S., Bais, A., Sablatnig, R.: A performance evaluation of SIFT and SURF for multispectral image matching. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012. LNCS, vol. 7324, pp. 166–173. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31295-3_20CrossRefGoogle Scholar
  11. 11.
    Karami, E., Prasad, S., Shehata, M.: Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. In: Proceedings of the 2015 Newfoundland Electrical and Computer Engineering Conference (2015)Google Scholar
  12. 12.
    Athani, S., Tejeshwar, C.H.: Performance analysis of key frame extraction using SIFT and SURF algorithms. IJCSIT Int. J. Comput. Sci. Inf. Technol 7(4), 2136–2139 (2016)Google Scholar
  13. 13.
    Zhu, W., Zeng, N., Wang, N.: Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS®. Health Care and Life Sciences, NESUG (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of TechnologyAgartalaIndia

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