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Invariants Feature Points Detection based on Random Sample Estimation

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 86 - Number 15
Year of Publication: 2014
Authors:
Kawther Abbas Sallal
Abdul-monem Saleh Rahma
10.5120/15059-3229

Kawther Abbas Sallal and Abdul-monem Saleh Rahma. Article: Invariants Feature Points Detection based on Random Sample Estimation. International Journal of Computer Applications 86(15):7-12, January 2014. Full text available. BibTeX

@article{key:article,
	author = {Kawther Abbas Sallal and Abdul-monem Saleh Rahma},
	title = {Article: Invariants Feature Points Detection based on Random Sample Estimation},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {86},
	number = {15},
	pages = {7-12},
	month = {January},
	note = {Full text available}
}

Abstract

Feature detection is the initial step in any image analysis procedure and is essential for the performance of computer vision applications like stereo vision, object recognition, object tracking systems. Research concerning the detection of feature points for different camera motion images in efficient and fast way. In this work, techniques of corner detection, geometric moments and random sampling are presented to simply and accurately locate the important feature points in image. For each extracted feature in image, a descriptor is calculated and based on the homograph transformation the matching is done. The results of experiments conducted on images taken by handheld camera and compared with the most famous SIFT method. The results show that the proposed algorithm is accurate, fast, efficient and robust under noise, transformation and compression circumstances.

References

  • Crowley, J. and Parker, A. 1984. A representation for shape based on peaks and ridges in the difference of low pass transform. IEEE Transactions on Pattern Analysis and Machine Intelligence. 6(2):156–170.
  • Alvarez, L. and Morales, F. 1997. Affine morphological multiscale analysis of corners and multiple junctions. International Journal of Computer Vision. 2(25):95–107.
  • Lindeberg, T. 1998. Feature detection with automatic scale selection. International Journal of Computer Vision. 30(2):79–116.
  • Lowe, D. G. 1999. Object recognition from local scale-invariant features. In Proceedings of the 7th International Conference on Computer Vision. Kerkyra. Greece. pp. 1150–1157.
  • Mikolajczyk, K. and Schmid, C. 2002. An affine invariant interest point detector. In Proceedings of the 7th European Conference on Computer Vision. Copenhagen. Denmark. vol. I. pp. 128–142.
  • Baumberg, A. (2000). Reliable feature matching across widely separated views. In Proceedings of the Conference on Computer Vision and Pattern Recognition. Hilton Head Island. South Carolina. USA. pp. 774–781.
  • Prashan, P. & Farzad, S. 2008. Feature based stereo correspondence using moment invariant. 4th International Conference on Information and Automation for Sustainability. Sustainable Development Through Effective Man-Machine Co-Existence. IEEE Region 10 and ICIAFS. Colombo. Sri Lanka. pp. 104-108.
  • Yinan, Y. , Kaiqi, H. and Tieniu, T. 2009. A Harris-like Scale Invariant Feature Detector. 9th Asian Conference on Computer Vision, Xi'an, September 23-27. Springer Berlin Heidelberg. pp. 586-595.
  • Tanvir, H. , Wei, T. , Guojun, L. and Martin L. 2010. An Enhancement to SIFT-Based Techniques for Image Registration. Dicta. International Conference on Digital Image Computing: Techniques and Applications. pp. 166-171.
  • Yijian, P. , Hao, W. , Jiang, Y. and Guanghui, C. 2010. Effective Image Registration based on Improved Harris Corner Detection. International Conference on Information, Networking and Automation (ICINA). 18-19 Oct. pp. 93-96. IEEE.
  • Harris, C. and Stephens, M. 1988. A combined corner and edge Detector. In Alvey Vision Conference. pp. 147–151.
  • Malik, J. , Dahiya, R. and Sainarayanan, G. 2011. Harris Operator Corner Detection using Sliding Window Method. International Journal of Computer Applications (0975 – 8887)Volume 22– No. 1. pp. 28-37.
  • Hu M. K. (1962). Visual pattern recognition by moments Invariants. IRE Trans. Information Theory, vol 8. pp:179- 87.
  • Mohamed, R. , Haniza, Y. , Puteh, S. , Ali, Y. , Abdul R. , S. , Mohd, R. , Sazali, Y. , Hazri, D. and Karthigayan, M. (2006). Object Detection using Geometric Invariant Moment. American Journal of Applied Sciences. vol 3 no. 6. pp: 1876-1878.
  • Bei, J. and Chen, L. (2011). Map Matching Algorithms Based on Invariant Moments. Journal of Computational Information Systems Vol. 7 (16). pp:5668-5673
  • Fischler M. and Bolles R. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, vol. 24. no. 6. pp:381–395.
  • Zuliani M. (2006). Computational Methods for Automatic Image Registration. PHD thesis. University of California, USA.