Abstract
We present a method for recovering the structure of a plant directly from a small set of widely-spaced images for automated analysis of phenotype. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is composed of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, without manual intervention.
Chapter PDF
Similar content being viewed by others
References
Vos, J., Evers, J.B., et al.: Functional structural plant modelling: a new versatile tool in crop science. Journal of Experimental Botany 61(8), 2101–2115 (2010)
Paulus, S., Dupuis, J., Mahlein, A.K., Kuhlmann, H.: Surface feature based classification of plant organs from 3d laserscanned point clouds for plant phenotyping. BMC Bioinformatics 14, 238 (2013)
Li, Y., Fan, X., et al.: Analyzing growing plants from 4d point cloud data. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2013) 32 (2013)
Bellasio, C., Olejníčková, J., et al.: Computer reconstruction of plant growth and chlorophyll fluorescence emission in three spatial dimensions. Sensors 12(1), 1052–1071 (2012)
Hartmann, A., Czauderna, T., et al.: HTPheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12, 148 (2011)
Golzarian, M.R., Frick, R.A., et al.: Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 1 (2011)
Lati, R.N., Filin, S., Eizenberg, H.: Estimating plant growth parameters using an energy minimization-based stereovision model. Computers and Electronics in Agriculture 98, 260–271 (2013)
Andersen, H.J., Reng, L., Kirk, K.: Geometric plant properties by relaxed stereo vision using simulated annealing. Computers and Electronics in Agriculture 49(2), 219–232 (2005)
Lati, R.N., Manevich, A., Filin, S.: Three-dimensional image-based modelling of linear features for plant biomass estimation. International Journal of Remote Sensing 34(17), 6135–6151 (2013)
Wang, H., Zhang, W., et al.: Image-based 3d corn reconstruction for retrieval of geometrical structural parameters. International Journal of Remote Sensing 30(20), 5505–5513 (2009)
Laga, H., Miklavcic, S.: Curve-based stereo matching for 3d modeling of plants. In: 20th International Congress on Modelling and Simulation (2013)
Kumar, P., Cai, J., Miklavcic, S.: High-throughput 3d modelling of plants for phenotypic analysis. In: Image and Vision Computing New Zealand, pp. 301–306. ACM (2012)
Alenyà, G., Dellen, B., Torras, C.: 3d modelling of leaves from color and ToF data for robotized plant measuring. In: IEEE International Conference on Robotics and Automation, pp. 3408–3414 (2011)
Preuksakarn, C., Boudon, F., et al.: Reconstructing plant architecture from 3d laser scanner data. In: 6th International Workshop on Functional-Structural Plant Models, pp. 16–18 (2010)
Quan, L., Tan, P., et al.: Image-based plant modeling. ACM Transactions on Graphics 25(3), 599–604 (2006)
Dornbusch, T., Wernecke, P., Diepenbrock, W.: A method to extract morphological traits of plant organs from 3d point clouds as a database for an architectural plant model. Ecological Modelling 200(12), 119–129 (2007)
Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(2), 150–162 (1994)
Guénard, J., Morin, G., Boudon, F., Charvillat, V.: Realistic plant modeling from images based on analysis-by-synthesis. In: Floater, M., Lyche, T., Mazure, M.-L., Mørken, K., Schumaker, L.L. (eds.) MMCS 2012. LNCS, vol. 8177, pp. 213–229. Springer, Heidelberg (2014)
Lopez, L.D., Ding, Y., Yu, J.: Modeling complex unfoliaged trees from a sparse set of images. Computer Graphics Forum 29(7), 2075–2082 (2010)
Huang, H., Mayer, H.: Generative statistical 3d reconstruction of unfoliaged trees from terrestrial images. Annals of GIS 15(2), 97–105 (2009)
Sturm, P.F., Maybank, S.J.: On plane-based camera calibration: A general algorithm, singularities, applications. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 432–437 (1999)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)
Cai, J., Miklavcic, S.: Automated extraction of three-dimensional cereal plant structures from two-dimensional orthographic images. IET Image Processing 6, 687–696 (2012)
Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Communications of the ACM 27(3), 236–239 (1984)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ward, B. et al. (2015). A Model-Based Approach to Recovering the Structure of a Plant from Images. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8928. Springer, Cham. https://doi.org/10.1007/978-3-319-16220-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-16220-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16219-5
Online ISBN: 978-3-319-16220-1
eBook Packages: Computer ScienceComputer Science (R0)