Ward, B.Bastian, J.van den Hengel, A.Pooley, D.Rajendra, B.Berger, B.Tester, M.Agapito, L.Bronstein, M.Rother, C.2018-06-062018-06-062015Lecture Notes in Artificial Intelligence, 2015 / Agapito, L., Bronstein, M., Rother, C. (ed./s), vol.8928, pp.215-23097833191621950302-97431611-3349http://hdl.handle.net/2440/112657ECCV 2014 Workshops, Part IV, LNCS 8928We 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.en© Springer International Publishing Switzerland 2015Plant phenotyping; image processing; plant architectureConference paper003002627310.1007/978-3-319-16220-1_160003618428000162-s2.0-84925430754182617van den Hengel, A. [0000-0003-3027-8364]Berger, B. [0000-0003-1195-4478]