Name
Abstract | ||
|---|---|---|
This article presents a model-based segmentation method applied to 3D data acquired on sunflower plants. Our objective is the quantification of the plant growth using observations made automatically from sensors moved around plants. Here, acquisitions are made on isolated plants: a 3D point cloud is computed using Structure from Motion with RGB images acquired all around a plant. Then the proposed method is applied in order to segment and label the plant leaves, i.e. to split up the point cloud in regions corresponding to plant organs: stem, petioles, and leaves. Every leaf is then reconstructed with NURBS and its area is computed from the triangular mesh. Our segmentation method is validated comparing these areas with the ones measured manually using a planimeter: it is shown that differences between automatic and manual measurements are less than 10%. The present results open interesting perspectives in direction of high-throughput sunflower phenotyping. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006126404590467 | PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 4 |
Keywords | Field | DocType |
3D Plant Phenotyping, Structure from Motion, Clustering, Labeling, Nurbs Fitting, Sunflowers | Structure from motion,Computer vision,Segmentation,Planimeter,Computer science,Sunflower,Artificial intelligence,RGB color model,Cluster analysis,Point cloud,Triangle mesh | Conference |
Citations | PageRank | References |
2 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| William Gelard | 1 | 2 | 0.40 |
| Michel Devy | 2 | 542 | 71.47 |
| Ariane Herbulot | 3 | 49 | 7.28 |
| Philippe Burger | 4 | 2 | 0.74 |