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...

Full description

Saved in:

Bibliographic Details
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
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.106803
record_format	uketd_dc
spelling	my-uitm-ir.1068032024-12-05T04:27:37Z Absorbing Markov chain saliency based variational selective active contour model for grayscale and vector-valued images / Muhammad Syukri Mazlin 2024 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 https://ir.uitm.edu.my/id/eprint/106803/ https://ir.uitm.edu.my/id/eprint/106803/1/106803.pdf text en public masters Universiti Teknologi MARA (UiTM) College of Computing, Informatics and Mathematics Jumaat, Abdul Kadir
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Jumaat, Abdul Kadir
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
qualification_level	Master's degree
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
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/106803/1/106803.pdf
_version_	1818588184996478976

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

Similar Items