Image pattern recognition by edge detection using discrete wavelet transforms

Authors

  • Ravikant Divakar Department of Physics, Hindu College, Moradabad, Affiliated to M.J.P. Rohilkhand University, Bareilly, India
  • Bijendra Singh Department of Physics, Hindu College, Moradabad, Affiliated to M.J.P. Rohilkhand University, Bareilly, India
  • Ashish Bajpai Department of Physics, Hindu College, Moradabad, Affiliated to M.J.P. Rohilkhand University, Bareilly, India
  • Anil Kumar Department of Physics, Hindu College, Moradabad, Affiliated to M.J.P. Rohilkhand University, Bareilly, India

DOI:

https://doi.org/10.31181/jdaic10029042022k

Keywords:

edge, image, approximation, difference, Haar, wavelet

Abstract

An edge is the high-frequency part of an image and represents the location where abrupt changes take place in the intensity of luminescence. Edge detection is a basic step in feature extraction and pattern recognition of any image. Wavelet transforms extract low- and high-frequency information from any signal separately. In a two-dimensional wavelet transformation, an image is decomposed into four sub-images: one approximation image and three different images (horizontal, vertical, and diagonal images) at each decomposition level. The difference images show how the neighboring pixels differ in the horizontal, vertical, and diagonal directions. The approximation coefficients are forced to zero, and the difference coefficients are inverse wavelet transformations, as the reconstructed image shows the edges of the image and describes its pattern. Using the Haar wavelet at decomposition levels 1, 2, and 3, image pattern recognition by edge detection is performed and discussed.

 

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References

Alarcon-Aquino, V., Ramirez-Cortes, J. M., Gomez-Gil, P., Starostenko, O., & Lobato-Morales, H. (2013). Lossy image compression using discrete wavelet transform and thresholding techniques. The Open Cybernetics & Systemics Journal, 7, 32-38.

Antoine, J. P. (1999). Wavelet analysis: A new tool in Physics. In: Van Den Berg, J.C. (ed) Wavelets in Physics. (pp. 9-22). Cambridge: Cambridge University Press.

Aydin, T., Yemez, Y., Anarim, E., & Sankur B. (1996). Multidirectional and multiscale edge detection via M-band wavelet transform. IEEE Transactions on Image Processing, 5(9), 1370-1377.

Canny, J. (2010). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679-698.

Coifman, R. R., & Wickerhauser, M. V. (1992). Entropy based algorithms for best basis selection. IEEE Transactions on Information Theory, 38(2), 713-718.

Daubechies, I. (1990). The wavelet transforms, time frequency localization and signal analysis. IEEE Transaction on Information Theory, 6, 961 – 1005.

Decoster, N., Roux, S. G., & Ameodo A. (2000). A wavelet-based method for multifractal image analysis. European Physical Journal B, 15(4), 739-764.

Gangetto, M., Magli, E., Martina, M., & Olmo, G. (2006). Optimization and implementation of the integer wavelet transform for image coding. IEEE Transactions on Image Processing, 11(2), 596-604.

Heil, C., & Walnut, D. (1989). Continuous and discrete wavelet transforms. SIAM review, 31, 628-666.

Hernandez, E., & Weiss, G. (1996). A First Course on Wavelets. New York: CRC Press.

Kharate, G. K., Patil, V. H., & Bhale, N. L. (2007). Selection of mother wavelet for image compression on basis of nature of image. Journal of Multimedia, 2(6), 44-51.

Kumar, A. (2017). Selection of optimal base wavelet for image compression. World Journal of Engineering, Research and Technology, 3(4), 396-405.

Kumar, A., Kumar, S., & Pathak, J. K. (2015). Spectral analysis of river Ramganga hydraulics using discrete wavelet transform. Proceedings of International Conference of Advance Research and Innovation (ICARI 2025), 370-373.

Kumar, A., Singh R., & Kumari, M. (2021). Spectral analysis of human face expressions and behaviour using wavelet transforms. Design Engineering, 21(4), 1628-1636.

Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693.

Prasad, L., & Iyenger, S. S. (1997). Wavelet Analysis with Application to Image Processing. New York: CRC Press.

Pratap, R. (2006). Getting Started with MATLAB7, 2nd Ed. Delhi: Oxford University Press.

Rioul, O., & Vetterli, M. (1991). Wavelets and signal processing. IEEE Signal Processing Magazine, 8(4), 14-38.

Santhosh, M., Charles, B. S., & Prasad M. N. G. (2012). Adaptive filter design for wavelet decomposition and reconstruction in image processing applications. ARPN Journal of Engineering and Applied Sciences, 7(3), 314- 318.

Soman, K. P. K., & Ramchandran I. (2005). Insight into Wavelets: From Theory to Practice. 2nd ed. Delhi: Prentice Hall of India.

Wickerhauser, M.V. (1994). Adaptive Wavelet Analysis from Theory to Software. Wellesley: A.K. Peters Limited.

Wei, J., Kin-Man L., & Ting-Zhi, S. (2009). Efficient Edge Detection Using Simplified Gabor Wavelets. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(4), 1036-1047.

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Published

29.04.2022

Issue

Vol. 2 No. 1 (2022): Journal of Decision Analytics and Intelligent Computing

Section

Articles

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Copyright (c) 2022 Ravikant Divakar, Bijendra Singh, Ashish Bajpai, Anil Kumar

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Image pattern recognition by edge detection using discrete wavelet transforms. (2022). Journal of Decision Analytics and Intelligent Computing, 2(1), 26-35. https://doi.org/10.31181/jdaic10029042022k