MARC details
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04835ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251125110924.0 |
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| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
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240909t20242024my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009949 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .W36 2024 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Wang Jing, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Model-based hybrid variational level set method applied to object detection in grey scale images / |
| Statement of responsibility, etc. |
Wang Jing |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
© 2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 314 pages : |
| Other physical details |
illustration ; |
| Materials specified |
0 cm. +30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Accurate segmentation of objects from grayscale images is fundamental in computer vision with diverse applications. In medical imaging, it enables precise detection of anatomical structures, lesions, and tumors from modalities like CT, MRI, and ultrasound for diagnosis, treatment planning, and monitoring diseases. In industrial inspection, segmentation algorithms detect product defects, cracks, or anomalies for quality control and safety. However, achieving accurate segmentation in grayscale images is a challenging endeavor due to several inherent complexities. Uneven illumination, a common issue in real-world imaging scenarios, can lead to significant variations in pixel intensities, obscuring object boundaries and complicating the segmentation process. Additionally, the presence of noise, whether from sensor imperfections or environmental factors, can further degrade image quality and introduce artifacts that hinder accurate segmentation. Moreover, complex object boundaries, particularly in scenes with occlusions, shadows, or intricate shapes, pose significant challenges for traditional segmentation methods. These methods often struggle to accurately delineate the intricate contours and regions of interest, leading to under-segmentation or over-segmentation errors. To address these challenges, the proposed thesis introduces a novel model-based hybrid variational level-set method, termed VKMHLS, specifically tailored for object detection in grayscale images. VKMHLS simplifies the Local Intensity Clustering (LIC) model and introduces a novel energy functional based on the region-based pressure function, enhancing the efficiency of segmentation for low grayscale images. Furthermore, a fast numerical implementation strategy enables swift segmentation of images and estimation of the offset field, significantly improving overall computational efficiency. To robustly and accurately segment intricate object structures in challenging grayscale datasets, VKMHLS employs a multi-layer model-based level-set structure with adaptive scale operators. These operators dynamically determine the optimal number of layers and precise scale parameters, overcoming issues with local minima and enabling successful handling of severely uneven grayscale distributions. Additionally, the thesis proposes an innovative active contour model called CER, which intelligently combines elements from the Chan-Vese (CV) model and the Region-Scalable Fitting (RSF) model. CER integrates information entropy calculations and minimizes the overall energy functional, allowing successful segmentation of regions with weak edges, strong noise interference, and uneven brightness variations across grayscale images. To tackle the persistent challenge of segmenting grayscale images with both uneven characteristics and high noise levels, a hybrid level-set algorithm based on kernel metrics is introduced. This algorithm leverages an improved multi-scale mean filter to mitigate grayscale inhomogeneity while reducing the impact of scale parameter selection. Kernel measurements and local similarity metrics suppress noise influence, enhancing robustness. Furthermore, a count gradient regularization term further reduces noise impact, ensuring precise segmentation results. Comprehensive experimental evaluations demonstrate that VKMHLS accurately segments grayscale images characterized by both inhomogeneity and noise contamination, exhibiting robust performance across various noise types. These attributes make VKMHLS a highly valuable tool for tackling realworld grayscale image segmentation challenges and enabling reliable object detection. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |