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3-Level Techniques Comparison based Image Recognition

by Zainab Ibrahim Abood, Ahlam Hanoon Al-sudani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 11
Year of Publication: 2014
Authors: Zainab Ibrahim Abood, Ahlam Hanoon Al-sudani
10.5120/17052-7241

Zainab Ibrahim Abood, Ahlam Hanoon Al-sudani . 3-Level Techniques Comparison based Image Recognition. International Journal of Computer Applications. 97, 11 ( July 2014), 19-25. DOI=10.5120/17052-7241

@article{ 10.5120/17052-7241,
author = { Zainab Ibrahim Abood, Ahlam Hanoon Al-sudani },
title = { 3-Level Techniques Comparison based Image Recognition },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 11 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 19-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number11/17052-7241/ },
doi = { 10.5120/17052-7241 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:23:51.419513+05:30
%A Zainab Ibrahim Abood
%A Ahlam Hanoon Al-sudani
%T 3-Level Techniques Comparison based Image Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 11
%P 19-25
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image recognition is one of the most important applications of information processing, in this paper; a comparison between 3-level techniques based image recognition has been achieved, using discrete wavelet (DWT) and stationary wavelet transforms (SWT), stationary-stationary-stationary (sss), stationary-stationary-wavelet (ssw), stationary-wavelet-stationary (sws), stationary wavelet-wavelet (sww), wavelet-stationary-stationary (wss), wavelet-stationary-wavelet (wsw), wavelet-wavelet-stationary (wws) and wavelet-wavelet-wavelet (www). A comparison between these techniques has been implemented. according to the peak signal to noise ratio (PSNR), root mean square error (RMSE), compression ratio (CR) and the coding noise e (n) of each third level. The two techniques that have the best results which are (sww and www) are chosen, then image recognition is applied to these two techniques using Euclidean distance and Manhattan distance and a comparison between them has been implemented. , it is concluded that, sww technique is better than www technique in image recognition because it has a higher match performance (100%) for Euclidean distance and Manhattan distance than that in www. .

References
  1. Matlab R2014a Computer Vision System Toolbox, "Recognition methods in image processing".
  2. Othman Khalif, "Wavelet Coding Design for Image Data Compression", The International Arab Journal of Information Technology, Vol. 2; No. 2; April 2005.
  3. Ale?s Proch´azka, Andrea Gavlasov´a, and Karel Volka, "Wavelet transform in image recognition", A. Prochazka@ieee. org.
  4. Dhaha Dia, Medien Zeghid, Taoufik Saidani, Mohamed Atri, Belgacem Bouallegue, Mohsen Machhout and Rached Tourki, "Multi-level Discrete Wavelet Transform Architecture Design", Proceedings of the World Congress on Engineering 2009 Vol I, July 1 - 3, 2009, London, U. K.
  5. Nikita Kashyap and G. R. SINHA, "Image Watermarking Using 3-Level Discrete Wavelet Transform (DWT)", I. J. Modern Education and Computer Science, April 2012 in MECS (http://www. mecs-press. org/), 50-56.
  6. Zainab Ibrahim Abood, Israa Jameel Muhsin and Nabeel Jameel Tawfiq, "Content-Based Image Retrieval (CBIR) Using Hybrid Technique", International Journal of Computer Applications Vol. 83 – No 12, December 2013, p17-24, zainab2012254@yahoo. com.
  7. Pratibha Sharma and Shanti Swami, "Digital Image Watermarking Using 3 level Discrete Wavelet Transform", Conference on Advances in Communication and Control Systems 2013, 129-133, pratibhasharma29@yahoo. com, shantiswamy@gmail. com.
  8. Zainab Ibraheem Abood, "Image Recognition using 3-D Two-LevelTechniques", Journal of Engineering, Vol. 19, No. 11, November 2013, 1407-1424, zainab2012254@yahoo. com.
  9. Matlab7. 8. 0 (R2009a) Image Processing Toolbox, "Video and Image Processing Blockset".
  10. Kanagaraj Kannan, Subramonian Arumuga Perumal and Kandasamy Arulmozhi, "Optimal Decomposition Level of Discrete, Stationary and Dual Tree Complex Wavelet Transform for Pixel based Fusion of Multi-focused Images", Serbian Journal of Electrical Engineering, Vol. 7, No. 1, May 2010,81-93,kannan_kcet@yahoo. co. in , visvenk@yahoo. co. in , principal@kcetvnr. org
  11. Y. Min, J. Zhtwei, Y. Wensheng, and Z. Xiaornin, "Applications of Generalized Learning in Image Recognition", IEEE, First International Conference on Neural Interface and Control Proceedings, 26-28 May 2005, 159-162.
Index Terms

Computer Science
Information Sciences

Keywords

3-level Techniques image recognition stationary wavelet transform wavelet transform feature extraction.

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