Original scientific paper

https://doi.org/10.17559/TV-20171126053407

A Local Density Shape Context Algorithm for Point Pattern Matching in Three Dimensional Space

You Zhou orcid.org/0000-0003-0013-1281 ; College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, 2699 Qianjin Street, Changchun, 130012, China
Panpan Cui ; College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, China
Yizhang Wang ; College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, China
Limin Wang ; School of Management Science and Information Engineering, Jilin University of Finance and Economics, 3699 Jingyue Street, Changchun, 130117, China
Sen Yang ; College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, China
Xu Zhou orcid.org/0000-0003-0013-1281 ; Center for Computer Fundamental Education, Jilin University, 2699 Qianjin Street, Changchun, 130012, China

Abstract

Three dimensional space point pattern matching technology shows significant usage in many scientific fields. It is a great challenge to match pairwise with rigid transformation in three dimensional space. In this paper, we propose an effect of Local Density Shape Context algorithm (LDSC). In LDSC, the point local density is firstly used for cutting down the negative impacting on extracting the feature descriptor. And the optimization of pairwise matching is firstly used in LDSC for improving the effectiveness. To demonstrate the performance of LDSC, we conduct experiments on synthetic datasets and real world datasets. The experimental results indicate that LDSC outperforms the three compared classical methods in most cases. LDSC is robust to outliers and noise.

Keywords

point local density; point pattern matching; shape context

Hrčak ID:

202626

URI

https://hrcak.srce.hr/202626

Publication date:

28.6.2018.

Visits: 1.728 *