We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. 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 made up 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, with no manual intervention.
@article{arxiv.1503.03191,
title = {A model-based approach to recovering the structure of a plant from images},
author = {Ben Ward and John Bastian and Anton van den Hengel and Daniel Pooley and Rajendra Bari and Bettina Berger and Mark Tester},
journal= {arXiv preprint arXiv:1503.03191},
year = {2015}
}