Name
Abstract | ||
|---|---|---|
Increased adoption of the systems approach to biological research has focused attention on the use of quantitative models of biological objects. This includes a need for realistic 3D representations of plant shoots for quantification and modelling. We present a fully automatic approach to image-based 3D plant reconstruction. The reconstructed plants are represented as a series of small planar sections that together model the more complex architecture of the leaf surfaces. The boundary of each leaf patch is refined using the level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed, and as such is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on datasets of wheat and rice plants, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-16220-1_12 | COMPUTER VISION - ECCV 2014 WORKSHOPS, PT IV |
Keywords | Field | DocType |
Plant phenotyping, Multi-view reconstruction, 3D, Level sets | Computer vision,Surface reconstruction,Curvature,Biological objects,Pattern recognition,Level set method,Computer science,Level set,Network topology,Planar,Artificial intelligence,Distance measures | Conference |
Volume | ISSN | Citations |
8928 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael P. Pound | 1 | 2 | 2.06 |
| Andrew P French | 2 | 31 | 5.71 |
| Erik H. Murchie | 3 | 2 | 0.37 |
| Tony P. Pridmore | 4 | 143 | 40.24 |