Division-Based Methods For Large Point Sets Registration
| dc.contributor.author | Chen, Junfen | |
| dc.date.accessioned | 2017-01-26T03:49:49Z | |
| dc.date.available | 2017-01-26T03:49:49Z | |
| dc.date.issued | 2016-03 | |
| dc.description.abstract | 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). Firstly, a subset-ICP method is proposed based on streaming division for rigid registration of large point sets. Instead of applying registration on the full point set as the original ICP does, it recovers the rotation and translation using only the correspondence between the pair of subsets. It iterates through all subset pairs until convergent. Secondly, streaming division is incorporated to CPD-B method for nonrigid registration of large point sets to recover the nonlinear displacement between each point pairs among the two sets. Unlike the subset-ICP, it extends each subset marginally to its neighbouring subset for robust point correspondence searching. The registration results of all subset pairs are directly merged. A heuristic search is also proposed to tune the width parameter of the Gaussian kernel in the original CPD method, namely S-CPD-B. Finally, bi-GMM-TPS is proposed as a two stage-based method for large non-rigid point set registration. It employs a clustering scheme to obtain clusters and registers the clusters’ centres using GMM-TPS to coarsely align the two full point sets. It then finely registers all cluster pairs using GMM-TPS again. Extensive experiments were conducted to validate the efficiency of the proposed methods on the publicly available datasets including very large point set from USF database. The experimental results demonstrated that the proposed methods are able to reduce the computational cost as well as maintaining registration errors at comparable level with the original methods. Three specific division-based registration methods are conceptually summarised as a division-based multi-stage registration framework for handling large non-rigid point sets registration. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/3594 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Point sets registration | en_US |
| dc.title | Division-Based Methods For Large Point Sets Registration | en_US |
| dc.type | Thesis | en_US |
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