Visual tracking for the recovery of multiple interacting plant root systems from X-ray ?CT images

Mairhofer, Stefan; Johnson, James; Sturrock, Craig; Bennett, Malcolm J.; Mooney, Sacha J.; Pridmore, Tony P.

Abstract

We propose a visual object tracking framework for the extraction of multiple interacting plant root systems from three-dimensional X-ray micro computed tomography images of plants grown in soil. Our method is based on a level set framework guided by a greyscale intensity distribution model to identify object boundaries in image cross-sections. Root objects are followed through the data volume, while updating the tracker's appearance models to adapt to changing intensity values. In the presence of multiple root systems, multiple trackers can be used, but need to distinguish target objects from one another in order to correctly associate roots with their originating plants. Since root objects are expected to exhibit similar greyscale intensity distributions, shape information is used to constrain the evolving level set interfaces in order to lock trackers to their correct targets. The proposed method is tested on root systems of wheat plants grown in soil.

Citation

Mairhofer, S., Johnson, J., Sturrock, C., Bennett, M. J., Mooney, S. J., & Pridmore, T. P. (2016). Visual tracking for the recovery of multiple interacting plant root systems from X-ray ?CT images. Machine Vision and Applications, 27(5), 721-734. https://doi.org/10.1007/s00138-015-0733-7

• Journal Article Type: Article
• Acceptance Date: Oct 12, 2015
• Online Publication Date: Dec 12, 2015
• Publication Date: 2016-07
• Deposit Date: Apr 26, 2016
• Publicly Available Date: Apr 26, 2016
• Journal: Machine Vision and Applications
• Print ISSN: 0932-8092
• Electronic ISSN: 1432-1769
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 5
• Pages: 721-734
• DOI: https://doi.org/10.1007/s00138-015-0733-7
• Keywords: Multiple object tracking, Root system recovery, Plant interaction, X-ray micro computed tomography
• Public URL: https://nottingham-repository.worktribe.com/output/769396
• Publisher URL: http://link.springer.com/article/10.1007/s00138-015-0733-7