Angle and Scale Invariant Template Matching for Handling Image Distortions

  • Authors

    • Badrinaathan J
    • L N.B.Srinivas
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12009
  • Template Matching, Filtering, Scale Invariant, Histogram Equalization, Angle Invariant, Cross Correlation

  • Abstract

    Template matching is a diagnostic approach for detecting a patch of a template image in a given source image. This plays a vital role in multitudinal computer vision applications. In this paper, we propose a methodology that makes the naive template matching algorithm scale and angle invariant during the image recognition process where the source and template is converted to gray scale which makes the technique enhance its proficiency. The proposed algorithm handles the arbitrary modulations of the image patch with respect to size and angle by an exhaustive search of all combinations of sizes are done along with populous combinations of angles. The images adapted are subjected to certain filtering and convolution methods which deepens the quality of the images which in turn assists in retrieving the features with accuracy. The image intensities are adjusted using histogram equalization to enhance the image contrast. These images are then subjected to perform template matching using normalized cross correlation to measure similarity between those two images.

     

     

  • References

    1. [1] Chang Liu, Yongqiang Bai, A new fast and robust template matching with randomness, ISSN: 1948-9447,July 2017

      [2] T. Dekel, S. Oron, M. Rubinstein, S. Avidan, and W. T. Freeman. Best-buddies similarity for robust template matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2021–2029, 2015.

      [3] M.-S. Choi and W.-Y. Kim. A novel two stage template matching method for rotation and illumination invariance. Pattern recognition, 35(1):119–129, 2002.

      [4] Ahmet Burak Yoldemir, Mehmet Sezgin ,Rotation and scale invariant template matching applied to buried object discrimination in gpr data, December 2010

      [5] Lowe D G. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2): 91-110, 2004.

      [6] M. Gharavi-Alkhansari. A fast globally optimal algorithm for template matching using low-resolution pruning. IEEE Transactions on Image Processing, 10(4):526–533, 2001.

      [7] J. Shi and C. Tomasi, “Good Features to Trackâ€, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1994.

      [8] J. P. Lewis,Fast Normalized Cross-Correlation, Industrial Light & Magi.

      [9] T. Padmapriya and V. Saminadan, “Improving Throughput for Downlink Multi user MIMO-LTE Advanced Networks using SINR approximation and Hierarchical CSI feedbackâ€, International Journal of Mobile Design Network and Innovation- Inderscience Publisher, ISSN : 1744-2850 vol. 6, no.1, pp. 14-23, May 2015.

      [10] S.V.Manikanthan and K.Baskaran “Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Networkâ€, CiiT International Journal of Programmable Device Circuits and Systems,Print: ISSN 0974 – 973X & Online: ISSN 0974 – 9624, Issue : November 2011, PDCS112011008

      [11] S.V. Manikanthan , T. Padmapriya “An enhanced distributed evolved node-b architecture in 5G tele-communications network†International Journal of Engineering & Technology (UAE), Vol 7 Issues No (2.8) (2018) 248-254.March2018

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  • How to Cite

    J, B., & N.B.Srinivas, L. (2018). Angle and Scale Invariant Template Matching for Handling Image Distortions. International Journal of Engineering & Technology, 7(2.24), 102-105. https://doi.org/10.14419/ijet.v7i2.24.12009

    Received date: 2018-04-24

    Accepted date: 2018-04-24

    Published date: 2018-04-25