Improved speeded up robust features for low contrast images

  • Authors

    • Adithya Subramanian Student
    • Sivagami M Assoc.Prof
    • Maheswari N Prof
    2018-10-13
    https://doi.org/10.14419/ijet.v7i4.17830
  • Speeded Up Robust Features, Low Contrast Images, Clustering, Contrast Limited Adaptive Histogram Equalization, Object Recognition, Contrast Enhancement, Feature Point Detection, Feature Descriptor Formation, Entropy.
  • The proposed work aims at improving the feature detection in Speeded up Robust Feature (SURF) Algorithm. It has been observed that the SURF feature detector shows low feature detection in low contrast images which is caused due to the application of weighted gaussian at multiple scales before feature point detection. To overcome this problem an effective pre-processing technique is proposed which increases the image contrast to an optimum level enabling detection of more features by SURF Algorithm. The paper also introduces an effective optimization which clusters the feature points describing the same region proposal and concatenating these feature points into a single feature point with a new region proposal which holds minimal region in common with other feature points to reduce the redundant feature points generated due to the application of pre-processing. Finally, to obtain the feature vector of the new region proposal of the feature point, the feature vectors of the feature points belonging to the same cluster are concatenated to form an arbitrary dimensional feature vector.

     

     

  • References

    1. [1] Bay, H., Tuytelaars, T. and Van Gool, L., 2006. Surf: Speeded up robust features. Computer vision–ECCV 2006, pp.404-417.

      [2] Wold, S., Esbensen, K. and Geladi, P., 1987. Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3), pp.37-52. https://doi.org/10.1016/0169-7439(87)80084-9.

      [3] Hyvärinen, A., Karhunen, J. and Oja, E., 2004. Independent component analysis (Vol. 46). John Wiley & Sons.

      [4] Lee, D.D. and Seung, H.S., 2001. Algorithms for non-negative matrix factorization. In Advances in neural information processing systems (pp. 556-562).

      [5] Goyal, S. and Benjamin, P., 2014. Object recognition using deep neural networks: A survey. arXiv preprint arXiv:1412.3684.

      [6] Lowe, D.G., 1999. Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on (Vol. 2, pp. 1150-1157). Ieee. https://doi.org/10.1109/ICCV.1999.790410.

      [7] Dalal, N. and Triggs, B., 2005, June. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE. https://doi.org/10.1109/CVPR.2005.177.

      [8] Leutenegger, S., Chli, M. and Siegwart, R.Y., 2011, November. BRISK: Binary robust invariant scalable keypoints. In Computer Vision (ICCV), 2011 IEEE International Conference on (pp. 2548-2555). IEEE.

      [9] Calonder, M., Lepetit, V., Strecha, C. and Fua, P., 2010. Brief: Binary robust independent elementary features. Computer Vision–ECCV 2010, pp.778-792. https://doi.org/10.1007/978-3-642-15561-1_56.

      [10] Alahi, A., Ortiz, R. and Vandergheynst, P., 2012, June. Freak: Fast retina keypoint. In Computer vision and pattern recognition (CVPR), 2012 IEEE conference on (pp. 510-517).

      [11] Kumar, S. and Singh, A., 2016. Image Processing for Recognition of Skin Diseases. International Journal of Computer Applications, 149(3).

      [12] Nivedha, M. and Aldo, M.S., 2015, Effective Dehazing with Conceptual Regularization Using Surf For Haze Image, International Journal of Engineering Sciences & Research Technology 4(3) pp. 379-384.

      [13] Prakash, S. and Gupta, P., 2013. An efficient ear recognition technique invariant to illumination and pose. Telecommunication Systems, pp.1-14. https://doi.org/10.1007/s11235-011-9621-2.

      [14] Sergieh, H.M., Egyed-Zsigmond, E., Doller, M., Coquil, D., Pinon, J.M. and Kosch, H., 2012, November. Improving surf image matching using supervised learning, Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on (pp. 230-237). IEEE.

      [15] S. M. Pizer, E. P. Amburn, J. D. Austin, et al.: Adaptive Histogram Equalization and Its Variations. Computer Vision, Graphics, and Image Processing 39 (1987) 355-368. https://doi.org/10.1016/S0734-189X(87)80186-X.

      [16] Min, B.S., Lim, D.K., Kim, S.J. and Lee, J.H., 2013. A novel method of determining parameters of CLAHE based on image entropy. International Journal of Software Engineering and Applications vol: 7 No: 5 pp. 113-120

      [17] Cheng, Jun (2017): brain tumour dataset. figshare.https://doi.org/10.6084/m9.figshare.1512427.v5, Retrieved: 16:47, Sep 27, 2017 (GMT)

      [18] Li, Q., 2006. Research on handmetric recognition and feature level fusion method (Doctoral dissertation, PhD thesis, BeiJing JiaoTong University, Beijing).

      [19] Adithya S., Sivagami M. (2018) Enhanced Scalar-Invariant Feature Transformation. In:Nandi A., Sujatha N., Menaka R., Alex J. (eds) Computational Signal Processing and Analysis. Lecture Notes in Electrical Engineering, vol 490. Springer, Singapore https://doi.org/10.1007/978-981-10-8354-9_32.

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  • How to Cite

    Subramanian, A., M, S., & N, M. (2018). Improved speeded up robust features for low contrast images. International Journal of Engineering & Technology, 7(4), 4697-4701. https://doi.org/10.14419/ijet.v7i4.17830