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DOI: 10.14569/IJACSA.2018.090333
PDF

2-D Object Recognition Approach using Wavelet Transform

Author 1: Kamelsh Kumar
Author 2: Riaz Ahmed Shaikh
Author 3: Rafaqat Hussain Arain
Author 4: Safdar Ali Shah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 3, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Humans have supernatural ability to observe, analyze, and tell about the layout of the 3D world with the help of their natural visual system. But contrary to machine vision system, it remains a most difficult task to recognize various objects from images being captured by cameras. This paper presents 2-D image object recognition approach using Daubechies (Db10) wavelet transform. Firstly, an edge detection is carried out to delineate objects from the images. Secondly, shape moments have been used for object recognition. For testing purpose, different geometrical shapes such as rectangle, circle, triangle and pattern have been selected for image analysis. Simulation has been performed using MATLAB, and obtained results showed that it accurately identifies the objects. The research goal was to test 2-D images for object recognition.

Keywords: Wavelet transforms; db10; edge detection; object recognition; shape moments

Kamelsh Kumar, Riaz Ahmed Shaikh, Rafaqat Hussain Arain and Safdar Ali Shah, “2-D Object Recognition Approach using Wavelet Transform” International Journal of Advanced Computer Science and Applications(IJACSA), 9(3), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090333

@article{Kumar2018,
title = {2-D Object Recognition Approach using Wavelet Transform},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090333},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090333},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {3},
author = {Kamelsh Kumar and Riaz Ahmed Shaikh and Rafaqat Hussain Arain and Safdar Ali Shah}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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