Division-Based Methods For Large Point Sets Registration
Pendaftaran set titik adalah satu langkah penting untuk mengukur persamaan antara dua set titik dan digunakan secara meluas dalam penglihatan komputer, grafik komputer, analisis imej perubatan, dan sebagainya. Peralatan semasa mampu menyediakan data dengan butiran terperinci sebagai set titik bes...
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| 格式: | Thesis |
| 語言: | 英语 |
| 出版: |
2016
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/31827/ |
| Abstract | Abstract here |
| _version_ | 1855629495425302528 |
|---|---|
| author | Chen , Junfen |
| author_facet | Chen , Junfen |
| author_sort | Chen , Junfen |
| description | Pendaftaran set titik adalah satu langkah penting untuk mengukur persamaan antara
dua set titik dan digunakan secara meluas dalam penglihatan komputer, grafik
komputer, analisis imej perubatan, dan sebagainya. Peralatan semasa mampu menyediakan
data dengan butiran terperinci sebagai set titik besar. Walau bagaimanapun,
prestasi kaedah pendaftaran konvensional menurun secara mendadak apabila saiz set
titik meningkat. Dalam tesis ini, tiga kaedah pendaftaran set titik terkenal dan antara
yang mempunyai prestasi terbaik dipertimbangkan untuk mengkaji pengubahan kaedah
konvensional kepada kaedah yang menangani pendaftaran set titik besar dengan
cekap. Kaedah-kaedah tersebut adalah Lelaran Titik Terdekat (ICP), Peralihan Titik
Bersambung (CPD) dan Model Campuran Gaussian berasaskan Plat-nipis Splin.
Point sets registration is a key step for measuring the similarity between two point
sets and widely used in various fields such as computer vision, computer graphics,
medical image analysis, to name a few. The current devices can capture data with
great details as large point set. However, conventional registration methods slow down
dramatically as the size of the point set increased. In this thesis, three well-known
and among-best-performance point sets registration methods incorporating division
schemes are considered to study transforming conventional methods to efficiently deal
with large point sets registration. These methods are Iterative Closest Point (ICP),
Coherent Point Drift (CPD), and Gaussian mixture models based on thin-plate splines
(GMM-TPS).
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| first_indexed | 2025-10-17T08:09:03Z |
| format | Thesis |
| id | usm-31827 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2025-10-17T08:09:03Z |
| publishDate | 2016 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | usm-318272019-04-12T05:25:22Z http://eprints.usm.my/31827/ Division-Based Methods For Large Point Sets Registration Chen , Junfen QA75.5-76.95 Electronic computers. Computer science Pendaftaran set titik adalah satu langkah penting untuk mengukur persamaan antara dua set titik dan digunakan secara meluas dalam penglihatan komputer, grafik komputer, analisis imej perubatan, dan sebagainya. Peralatan semasa mampu menyediakan data dengan butiran terperinci sebagai set titik besar. Walau bagaimanapun, prestasi kaedah pendaftaran konvensional menurun secara mendadak apabila saiz set titik meningkat. Dalam tesis ini, tiga kaedah pendaftaran set titik terkenal dan antara yang mempunyai prestasi terbaik dipertimbangkan untuk mengkaji pengubahan kaedah konvensional kepada kaedah yang menangani pendaftaran set titik besar dengan cekap. Kaedah-kaedah tersebut adalah Lelaran Titik Terdekat (ICP), Peralihan Titik Bersambung (CPD) dan Model Campuran Gaussian berasaskan Plat-nipis Splin. Point sets registration is a key step for measuring the similarity between two point sets and widely used in various fields such as computer vision, computer graphics, medical image analysis, to name a few. The current devices can capture data with great details as large point set. However, conventional registration methods slow down dramatically as the size of the point set increased. In this thesis, three well-known and among-best-performance point sets registration methods incorporating division schemes are considered to study transforming conventional methods to efficiently deal with large point sets registration. These methods are Iterative Closest Point (ICP), Coherent Point Drift (CPD), and Gaussian mixture models based on thin-plate splines (GMM-TPS). 2016-03 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/31827/1/CHEN_JUNFEN_24%28NN%29.pdf Chen , Junfen (2016) Division-Based Methods For Large Point Sets Registration. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Chen , Junfen Division-Based Methods For Large Point Sets Registration |
| thesis_level | PhD |
| title | Division-Based Methods For Large Point Sets Registration |
| title_full | Division-Based Methods For Large Point Sets Registration |
| title_fullStr | Division-Based Methods For Large Point Sets Registration |
| title_full_unstemmed | Division-Based Methods For Large Point Sets Registration |
| title_short | Division-Based Methods For Large Point Sets Registration |
| title_sort | division based methods for large point sets registration |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/31827/ |
| work_keys_str_mv | AT chenjunfen divisionbasedmethodsforlargepointsetsregistration |