Authors:
William Gelard
1
;
Michel Devy
2
;
Ariane Herbulot
3
and
Philippe Burger
4
Affiliations:
1
CNRS, LAAS, Univ. de Toulouse, INRA and AGIR, France
;
2
CNRS and LAAS, France
;
3
CNRS, LAAS and Univ. de Toulouse, France
;
4
INRA and AGIR, France
Keyword(s):
3D Plant Phenotyping, Structure from Motion, Clustering, Labeling, Nurbs Fitting, Sunflowers.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image-Based Modeling
;
Pattern Recognition
;
Segmentation and Grouping
;
Shape Representation and Matching
;
Software Engineering
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.