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Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin

Image segmentation plays a crucial role in various fields, including medical image analysis, pattern recognition, computer vision and biometric identification where accurate delineation of desired objects by the image segmentation model is essential. The well-known image segmentation model namely va...

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Main Author: Mazlin, Muhammad Syukri
Format: Thesis
Language:English
Published: 2024
Subjects:
Pattern recognition systems
Online Access:https://ir.uitm.edu.my/id/eprint/106803/1/106803.pdf
https://ir.uitm.edu.my/id/eprint/106803/
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spelling my.uitm.ir.1068032024-12-05T04:27:37Z https://ir.uitm.edu.my/id/eprint/106803/ Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin Mazlin, Muhammad Syukri Pattern recognition systems Image segmentation plays a crucial role in various fields, including medical image analysis, pattern recognition, computer vision and biometric identification where accurate delineation of desired objects by the image segmentation model is essential. The well-known image segmentation model namely variational Active Contour (AC) model is capable in partitioning or segmenting objects in an input image effectively. To partition or segment a specific object in an input image, the selective type of AC model segmentation approach is preferable as compared to the global type of AC model. However, when it comes to segment grayscale or vector-valued (color) images with inhomogeneous intensity, the existing selective AC models often yield unsatisfactory results. To address this issue, two studies namely Study 1 and Study 2 are designed in this research which aim to propose a new variational selective AC model for grayscale and vector-valued images respectively by integrating the saliency image map and the local image fitting concepts. By leveraging the saliency image map via Absorbing Markov Chain approach, the model enhances the focus on desired objects while suppressing the influence of intensity inhomogeneity. In addition, the presence of the local image fitting helps in dealing with intensity inhomogeneity problem by effectively capturing local image characteristics. 2024 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/106803/1/106803.pdf Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin. (2024) Masters thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/106803.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Pattern recognition systems
spellingShingle Pattern recognition systems
Mazlin, Muhammad Syukri
Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin
description Image segmentation plays a crucial role in various fields, including medical image analysis, pattern recognition, computer vision and biometric identification where accurate delineation of desired objects by the image segmentation model is essential. The well-known image segmentation model namely variational Active Contour (AC) model is capable in partitioning or segmenting objects in an input image effectively. To partition or segment a specific object in an input image, the selective type of AC model segmentation approach is preferable as compared to the global type of AC model. However, when it comes to segment grayscale or vector-valued (color) images with inhomogeneous intensity, the existing selective AC models often yield unsatisfactory results. To address this issue, two studies namely Study 1 and Study 2 are designed in this research which aim to propose a new variational selective AC model for grayscale and vector-valued images respectively by integrating the saliency image map and the local image fitting concepts. By leveraging the saliency image map via Absorbing Markov Chain approach, the model enhances the focus on desired objects while suppressing the influence of intensity inhomogeneity. In addition, the presence of the local image fitting helps in dealing with intensity inhomogeneity problem by effectively capturing local image characteristics.
format Thesis
author Mazlin, Muhammad Syukri
author_facet Mazlin, Muhammad Syukri
author_sort Mazlin, Muhammad Syukri
title Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin
title_short Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin
title_full Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin
title_fullStr Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin
title_full_unstemmed Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin
title_sort absorbing markov chain saliency based variational selective active contour model for grayscale and vector-valued images / muhammad syukri mazlin
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/106803/1/106803.pdf
https://ir.uitm.edu.my/id/eprint/106803/
_version_ 1817847371696963584
score 13.222552

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