A Model-Based Approach to Recovering the Structure of a Plant from Images

Ben Ward, John Bastian, Anton van den Hengel, Daniel Pooley, Rajendra Bari, Bettina Berger, Mark A. Tester

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2014 Workshops
PublisherSpringer Nature
Number of pages16
ISBN (Print)9783319162195
StatePublished - Mar 19 2015

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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