Division-Based Methods For Large Point Sets Registration

• Main Author: Chen , Junfen
• Format: Thesis
• Language: English
• Published: 2016
• Subjects: QA75.5-76.95 Electronic computers. Computer science
• Online Access: http://eprints.usm.my/31827/1/CHEN_JUNFEN_24%28NN%29.pdf; http://eprints.usm.my/31827/
• Institution: Universiti Sains Malaysia

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).