Name
Abstract | ||
|---|---|---|
The emerging discipline of plant phenomics aims to measure key plant characteristics, or traits, though as yet the set of plant traits that should be measured by automated systems is not well defined. Methods capable of recovering generic representations of the 3D structure of plant shoots from images would provide a key technology underpinning quantification of a wide range of current and future physiological and morphological traits. We present a fully automatic approach to image-based 3D plant reconstruction which represents plants as series of small planar sections that together model the complex architecture of leaf surfaces. The initial boundary of each leaf patch is refined using a 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. As such it 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 real images of wheat and rice plants, an artificial plant with challenging architecture, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy. We also illustrate the method’s potential to support the identification of individual leaves, and so the phenotyping of plant shoots, using a spectral clustering approach. |
Year | DOI | Venue |
|---|---|---|
2016 | https://doi.org/10.1007/s00138-016-0756-8 | Mach. Vis. Appl. |
Keywords | Field | DocType |
Plant phenotyping,Multi-view reconstruction,3D,Level sets | Spectral clustering,Phenomics,Curvature,Pattern recognition,Level set method,Computer science,Level set,Network topology,Artificial intelligence,Real image,Distance measures | Journal |
Volume | Issue | ISSN |
27 | 5 | 0932-8092 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael P. Pound | 1 | 2 | 2.06 |
| Andrew P French | 2 | 31 | 5.71 |
| John A. Fozard | 3 | 0 | 0.34 |
| Erik H. Murchie | 4 | 0 | 0.68 |
| Tony P. Pridmore | 5 | 143 | 40.24 |