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DOI: 10.14569/IJACSA.2016.071126
PDF

Wavelet based Scalable Edge Detector

Author 1: Imran Touqir
Author 2: Adil Masood Siddique
Author 3: Yasir Saleem

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 11, 2016.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Fixed size kernels are used to extract differential structure of images. Increasing the kernal size reduces the localization accuracy and noise along with increase in computational complexity. The computational cost of edge extraction is related to the image resolution or scale. In this paper wavelet scale correlation for edge detection along with scalability in edge detector has been envisaged. The image is decomposed according to its resolution, structural parameters and noise level by multilevel wavelet decomposition using Quadrature Mirror Filters (QMF). The property that image structural information is preserved at each decomposition level whereas noise is partially reduced within subbands, is being exploited. An innovative wavelet synthesis approach is conceived based on scale correlation of the concordant detail bands such that the reconstructed image fabricates an edge map of the image. Although this technique falls short to spot few edge pixels at contours but the results are better than the classical operators in noisy scenario and noise elimination is significant in the edge maps keeping default threshold constraint.

Keywords: Wavelet scales correlation; Edge detection; image denoising; Multiresolution analysis; entropy reduction

Imran Touqir, Adil Masood Siddique and Yasir Saleem, “Wavelet based Scalable Edge Detector” International Journal of Advanced Computer Science and Applications(IJACSA), 7(11), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071126

@article{Touqir2016,
title = {Wavelet based Scalable Edge Detector},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071126},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071126},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {11},
author = {Imran Touqir and Adil Masood Siddique and Yasir Saleem}
}



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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