Finding rigid sub-structure patterns from 3D point-sets
| Autor: | Zheshuo Li, Zihe Chen, Hu Ding, Nitasha Sehgal, Andrew Fritz, Ziyun Huang, Danyang Chen, Jinhui Xu, Ronald Berezney |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
business.industry Structure (category theory) Motion detection Combinatorics 03 medical and health sciences 030104 developmental biology Match moving Cluster (physics) Substructure Partition (number theory) Point (geometry) Artificial intelligence business Cluster analysis Algorithm Mathematics |
| Zdroj: | ICPR |
| Popis: | In this paper, we study the following rigid substructure pattern reconstruction problem: given a set of n input structures (i.e. point-sets), partition each structure into k rigid sub-structures so that the nk rigid substructures can be grouped into k clusters with each of them containing exact one rigid substructure from every input structure and the total clustering cost is minimized, where the clustering cost of a cluster is the total distance between a pattern reconstructed for this cluster and every member rigid substructure. Different from most of the existing models for pattern reconstruction (where each input point-set is often treated as a single structure), our model views each input point-set as a collection of k rigid substructures, and aims to extract similar rigid substructures from each input point-set to form k rigid clusters. The problem is motivated by an interesting biological application for determining the topological structure of chromosomes inside the cell nucleus. We propose a highly effective and practical solution based on a number of new insights to pattern reconstruction, clustering, and motion detection. We validate our method on synthetic, biological and motion tracking datasets. Experimental results suggest that our approach yields a near optimal solution. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|