An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a par...

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Main Author: Ismael, Ahmed Naser
Format: Thesis
Language:eng
eng
Published: 2016
Subjects:
QA71-90 Instruments and machines
Online Access:https://etd.uum.edu.my/5625/1/s813728_01.pdf
https://etd.uum.edu.my/5625/2/s813728_02.pdf
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id my-uum-etd.5625
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Yusof, Yuhanis
topic QA71-90 Instruments and machines
spellingShingle QA71-90 Instruments and machines
Ismael, Ahmed Naser
An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
description Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold and the use of Euclidean Distance as distance measure. Such an approach leads to a weak reliability and shape matching of the produced segments. Hence, this study proposes an Improved Fast Scanning algorithm that is based on Sorensen distance measure and adaptive threshold function. The proposed adaptive threshold function is based on the grey value in an image’s pixels and variance. The proposed Improved Fast Scanning algorithm is realized on two datasets which contains images of cars and nature. Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. Experimental results showed that proposed algorithm produced higher PSNR compared to the standard Fast Scanning. Such a result indicate that the proposed Improved Fast Scanning algorithm is useful in image segmentation and later contribute in identifying region of interesting in pattern recognition.
format Thesis
qualification_name masters
qualification_level Master's degree
author Ismael, Ahmed Naser
author_facet Ismael, Ahmed Naser
author_sort Ismael, Ahmed Naser
title An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
title_short An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
title_full An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
title_fullStr An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
title_full_unstemmed An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
title_sort improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2016
url https://etd.uum.edu.my/5625/1/s813728_01.pdf
https://etd.uum.edu.my/5625/2/s813728_02.pdf
_version_ 1747827959769595904
spelling my-uum-etd.56252021-04-05T02:41:01Z An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation 2016 Ismael, Ahmed Naser Yusof, Yuhanis Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold and the use of Euclidean Distance as distance measure. Such an approach leads to a weak reliability and shape matching of the produced segments. Hence, this study proposes an Improved Fast Scanning algorithm that is based on Sorensen distance measure and adaptive threshold function. The proposed adaptive threshold function is based on the grey value in an image’s pixels and variance. The proposed Improved Fast Scanning algorithm is realized on two datasets which contains images of cars and nature. Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. Experimental results showed that proposed algorithm produced higher PSNR compared to the standard Fast Scanning. Such a result indicate that the proposed Improved Fast Scanning algorithm is useful in image segmentation and later contribute in identifying region of interesting in pattern recognition. 2016 Thesis https://etd.uum.edu.my/5625/ https://etd.uum.edu.my/5625/1/s813728_01.pdf text eng public https://etd.uum.edu.my/5625/2/s813728_02.pdf text eng public masters masters Universiti Utara Malaysia Abbas, K., & Rydh, M. (2012). Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm. International Journal of Image, Graphics and Signal Processing, 4(10), 48–53. Abdullah, S. N. H. S., Khalid, M., Yusof, R., & Omar, K. (2007). Comparison of Feature Extractors in License Plate Recognition. IEEE First Asia International Conference on Modelling & Simulation (AMS’07) (pp. 7–10). Phuket,Thailand. http://doi.org/10.1109/AMS. 2007.25 Abed, M. (2011). Recognition of Different Size Arabic Isolated Characters Using Genetic Algorithm. 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