A model-based approach to recovering the structure of a plant from images
Date
2015
Authors
Ward, B.
Bastian, J.
van den Hengel, A.
Pooley, D.
Rajendra, B.
Berger, B.
Tester, M.
Editors
Agapito, L.
Bronstein, M.
Rother, C.
Bronstein, M.
Rother, C.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Artificial Intelligence, 2015 / Agapito, L., Bronstein, M., Rother, C. (ed./s), vol.8928, pp.215-230
Statement of Responsibility
Ben Ward, John Bastian, Anton van den Hengel, Daniel Pooley, Rajendra Bari, Bettina Berger, and Mark Tester
Conference Name
13th European Conference on Computer Vision Workshops (ECCV 2014) (6 Sep 2014 - 7 Sep 2014 : Zurich, Switzerland)
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.
School/Discipline
Dissertation Note
Provenance
Description
ECCV 2014 Workshops, Part IV, LNCS 8928
Access Status
Rights
© Springer International Publishing Switzerland 2015