Name
Abstract | ||
|---|---|---|
In this paper, an interacting multiple models (IMM) motion filter based local graph matching method is proposed to track the plant cells, by exploiting the tight spatial topology of neighboring cells in a multicellular field as contextual information. The IMM filter is used to predict the movement of the cells, and then the local graph matching approach is used to search the target cells in the local neighborhood of the predicted position. The combination of the IMM filter and local graph matching greatly reduces the size of the searching region in the matching process and enhances the tracking stability as well. Furthermore, the cells' lineages are generated by using a maximum-a-posteriori (MAP) lineage association method. The effectiveness and efficiency of the proposed tracking method are validated by experiments on real plant cell datasets. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Cell tracking, IMM filter, local graph matching, tracklets association |
Field | DocType | ISSN |
Contextual information,Pattern recognition,Computer science,Matching (graph theory),Artificial intelligence,Multiple Models | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Liu | 1 | 1 | 2.71 |
| Yue He | 2 | 105 | 16.62 |
| Jieqin Li | 3 | 0 | 0.68 |
| Xiao-Yan Liu | 4 | 22 | 11.04 |
| Hongzhong Zhang | 5 | 0 | 1.69 |