Implementation of SIFT for detection of electronic waste

  • Authors

    • A Roshna Meeran
    • V Nithya
    https://doi.org/10.14419/ijet.v7i2.8.10461

    Received date: March 22, 2018

    Accepted date: March 22, 2018

    Published date: March 19, 2018

  • Electronic waste, HSV, Object recognition, Scale Invariant feature transform.

  • Abstract

    The paper focuses on the investigation of image processing of Electronic waste detection and identification in recycling process of all Electronic items. Some of actually collected images of E-wastes would be combined with other wastes. For object matching with scale in-variance the SIFT (Scale -Invariant- Feature Transform) is applied. This method detects the electronic waste found among other wastes and also estimates the amount of electronic waste detected the give set of wastes. The detection of electronics waste by this method is most efficient ways to detect automatically without any manual means.

  • References

    1. Shivakanth,Archana Mane, “Object Recognition using SIFT” IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 4, June 2014. ISSN 2348 – 7968
    2. David Lowe, “The SIFT (Scale Invariant Feature Transform) Detector and Descriptor” University of British Columbia Initial paper ICCV 1999 Newer journal paper IJCV 2004
    3. Sachin V. Sinkar, Dr. Ashwini M. Deshpande, “object recognition with plain background by using ann and sift based features” 2015 International Conference on Information Processing (ICIP)Vishwakarma Institute of Technology. Dec 16-19, 2015
    4. Florin AlexandruPavel, Zhiyong Wang and David Dagan Feng, “Reliable Object Recognition using SIFT Features,” IEEE International workshop on Multimedia Signal Processing, pp. 1-6, October 2009
    5. Zhou, Q. and Zhao, Z. (2012). “Substation equipment image recognition based on sift feature matching.” 2012 5th International Congress on Image and Signal Processing, 1344–1347 (Oct).
    6. R. Fergus, P. Perona and A. Zisserman, “Object Class Recognition by Unsupervised Scale-Invariant Learning,” in Proceedings of CVPR, pp. 264-271, 2003.
    7. Ballard, D.H., “Generalizing the Hough transform to Detect arbitrary patterns,” Pattern Recognition, 13, 2 (1981), pp.111-122.
    8. [8]Basri, Ronen, and David.W. Jacobs, “Recognition using region correspondences,” International Journal of Computer Vision, 25, 2 (1996), pp. 141–162.
    9. Andrea Vedaldi “An open implementation of the SIFT detector and descriptor”UCLA CSD Technical Report 070012, (2007)
    10. Sarvaiya, J. N., Patnaik, S., and Bombaywala, S. (2009). “Image registration by template matching using normalized cross-correlation.” 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 819–822 (Dec).
    11. Rafael C. G., Richard E. W., and Steven L. E., “Digital Image Processing Using MATLAB,” McGraw-Hill, 2008
    12. Kong Songtao , Kong Songtao “Identification Method of Waste Based on Gray Level Co-occurrence Matrix and Neural Network” IEEE 2011
    13. Stefano, L. D., Mattoccia, S., and Mola, M. (2003). “An efficient algorithm for exhaustive template matching based on normalized cross correlation.” 12th International Conference on Image Analysis and Processing, 2003.Proceedings.322–327 (Sept).
    14. Koen E.A. van de Sande, Theo Gevers and Cees G.M. Snoek, “Evaluating Color Descriptors for Object and Scene Recognition,” IEEE Transactions on PatternAnalysis and Machine Intelligence, Vol. 32, No. 9, pp. 1582-1596, September 2010.
    15. Li, L., Huang, W., Gu, I. Y.-H., and Tian, Q. (2004). “Statistical modeling of complex backgrounds for foreground objects detection.” IEEE Transactions on Image Processing, 13(11), 1459–1472.
    16. Ardeshir Goshtasby “Template Matching in Rotated Images” IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. Pami-7, No. 3, May 1985
    17. S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb. 2015,Publisher: IEEE,DOI: 10.1109/ECS.2015.7124833.
    18. T. Padmapriya and V. Saminadan, “Priority based fair resource allocation and Admission Control Technique for Multi-user Multi-class downlink Traffic in LTE-Advanced Networks”, International Journal of Advanced Research, vol.5, no.1, pp.1633-1641, January 2017.

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  • How to Cite

    Roshna Meeran, A., & Nithya, V. (2018). Implementation of SIFT for detection of electronic waste. International Journal of Engineering and Technology, 7(2.8), 353-357. https://doi.org/10.14419/ijet.v7i2.8.10461