Hierarchical K-means clustering for registration of multi-view point sets
| Autor: | Jinqian Chen, Rui Guo, Lin Wang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science k-means clustering Recursive partitioning computer.software_genre Hierarchical clustering Control and Systems Engineering Robustness (computer science) Benchmark (computing) Data mining Granularity Electrical and Electronic Engineering Cluster analysis Focus (optics) computer |
| Zdroj: | Computers & Electrical Engineering. 94:107321 |
| ISSN: | 0045-7906 |
| DOI: | 10.1016/j.compeleceng.2021.107321 |
| Popis: | As a long-standing research issue in computer vision and robotics, multi-view registration has attracted much attention in recent years. Most existing works are mainly focus on the estimating the point to point match correspondence, which usually suffers from the poor initial pose and data noise as well as leads to the inaccurate matches. To overcome the aforementioned limitation, we propose a novel Hierarchical K-means Clustering Registration (HKCR), which casts the multi-view registration as a hierarchical clustering task. Specifically, the proposed method employs a small number of clusters firstly, then increases the number of clusters during the registration process. Benefiting from the recursive partitioning process, more robust and more accurate results can be achieved with the increasing finer granularity. To show the effectiveness and robustness of HKCR, extensive experiments are conducted on several benchmark datasets and compared to several state-of-the-art methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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