Wavelet-based edge detection

There are existing edge detection systems that are robust and effective. The most common algorithms for these systems are the Gradient and Laplacian method. Nonetheless, these algorithms have difficulties in detecting edges when the difference in contrast between the target and the background is low...

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Main Authors: Dimla, Celine Angela G., Reyes, Jocelyn T., Vallejo, Kristine N., Martinez, Glenn Paul S.
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Language:English
Published: Animo Repository 2004
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/14229
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-148712021-11-12T06:20:18Z Wavelet-based edge detection Dimla, Celine Angela G. Reyes, Jocelyn T. Vallejo, Kristine N. Martinez, Glenn Paul S. There are existing edge detection systems that are robust and effective. The most common algorithms for these systems are the Gradient and Laplacian method. Nonetheless, these algorithms have difficulties in detecting edges when the difference in contrast between the target and the background is low. Another trouble area is that they are quite susceptible to noise. The Gradient and Laplacian method are implemented in this system on low contrast images with different noise levels. In an attempt to discover a better alternative, the Wavelet algorithm was also applied to the edge detection principle. A comparative study on the speed and accuracy in the detection of edges was then performed on the three algorithms. The speed and accuracy results were quantified through the MATLAB's Stopwatch Timer function and the Pratt's Figure of Merit formula respectively. The Gradient algorithm had the fastest run-time and the Laplacian algorithm had detected the most number of accurate edges when there is no visual noise in the image. Interestingly, the Wavelet algorithm appeared to be intermediate in both its speed and its speed and its accuracy. It was also noted through visual inspection that the Bioorthogonal basis function of the Wavelet algorithm was best suited for real world images. 2004-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/14229 Bachelor's Theses English Animo Repository Computer algorithms Wavelets (Mathematics) Image procesings--Digital techniques Imaging systems Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Computer algorithms
Wavelets (Mathematics)
Image procesings--Digital techniques
Imaging systems
Computer Sciences
spellingShingle Computer algorithms
Wavelets (Mathematics)
Image procesings--Digital techniques
Imaging systems
Computer Sciences
Dimla, Celine Angela G.
Reyes, Jocelyn T.
Vallejo, Kristine N.
Martinez, Glenn Paul S.
Wavelet-based edge detection
description There are existing edge detection systems that are robust and effective. The most common algorithms for these systems are the Gradient and Laplacian method. Nonetheless, these algorithms have difficulties in detecting edges when the difference in contrast between the target and the background is low. Another trouble area is that they are quite susceptible to noise. The Gradient and Laplacian method are implemented in this system on low contrast images with different noise levels. In an attempt to discover a better alternative, the Wavelet algorithm was also applied to the edge detection principle. A comparative study on the speed and accuracy in the detection of edges was then performed on the three algorithms. The speed and accuracy results were quantified through the MATLAB's Stopwatch Timer function and the Pratt's Figure of Merit formula respectively. The Gradient algorithm had the fastest run-time and the Laplacian algorithm had detected the most number of accurate edges when there is no visual noise in the image. Interestingly, the Wavelet algorithm appeared to be intermediate in both its speed and its speed and its accuracy. It was also noted through visual inspection that the Bioorthogonal basis function of the Wavelet algorithm was best suited for real world images.
format text
author Dimla, Celine Angela G.
Reyes, Jocelyn T.
Vallejo, Kristine N.
Martinez, Glenn Paul S.
author_facet Dimla, Celine Angela G.
Reyes, Jocelyn T.
Vallejo, Kristine N.
Martinez, Glenn Paul S.
author_sort Dimla, Celine Angela G.
title Wavelet-based edge detection
title_short Wavelet-based edge detection
title_full Wavelet-based edge detection
title_fullStr Wavelet-based edge detection
title_full_unstemmed Wavelet-based edge detection
title_sort wavelet-based edge detection
publisher Animo Repository
publishDate 2004
url https://animorepository.dlsu.edu.ph/etd_bachelors/14229
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