Name
Abstract | ||
|---|---|---|
Studying growth and development of plants is of central importance in botany. Current quantitative are either limited to tedious and sparse manual measurements, or coarse image-based 2D measurements. Availability of cheap and portable 3D acquisition devices has the potential to automate this process and easily provide scientists with volumes of accurate data, at a scale much beyond the realms of existing methods. However, during their development, plants grow new parts (e.g., vegetative buds) and bifurcate to different components --- violating the central incompressibility assumption made by existing acquisition algorithms, which makes these algorithms unsuited for analyzing growth. We introduce a framework to study plant growth, particularly focusing on accurate localization and tracking topological events like budding and bifurcation. This is achieved by a novel forward-backward analysis, wherein we track robustly detected plant components back in time to ensure correct spatio-temporal event detection using a locally adapting threshold. We evaluate our approach on several groups of time lapse scans, often ranging from days to weeks, on a diverse set of plant species and use the results to animate static virtual plants or directly attach them to physical simulators. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2508363.2508368 | ACM Trans. Graph. |
Keywords | Field | DocType |
central importance,plant species,static virtual plant,acquisition device,plant growth,plant component,acquisition algorithm,accurate localization,point cloud data,accurate data,Studying growth | Computer vision,Data mining,Artificial intelligence,Point cloud,Mathematics | Journal |
Volume | Issue | ISSN |
32 | 6 | 0730-0301 |
Citations | PageRank | References |
29 | 1.19 | 42 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yangyan Li | 1 | 489 | 17.04 |
| Xiaochen Fan | 2 | 40 | 3.74 |
| Niloy J. Mitra | 3 | 3813 | 176.15 |
| Daniel Chamovitz | 4 | 29 | 1.19 |
| Daniel Cohen-Or | 5 | 10588 | 533.55 |
| Baoquan Chen | 6 | 2095 | 111.30 |