DocumentCode
73545
Title
Non-destructive automatic leaf area measurements by combining stereo and time-of-flight images
Author
Yu Song ; Glasbey, Chris A. ; Polder, Gerrit ; van der Heijden, Gerie W. A. M.
Author_Institution
Biomath. & Stat. Scotland, Edinburgh, UK
Volume
8
Issue
5
fYear
2014
fDate
Oct-14
Firstpage
391
Lastpage
403
Abstract
Leaf area measurements are commonly obtained by destructive and laborious practice. This study shows how stereo and time-of-flight (ToF) images can be combined for non-destructive automatic leaf area measurements. The authors focus on some challenging plant images captured in a greenhouse environment, and show that even the state-of-the-art stereo methods produce unsatisfactory results. By transforming depth information in a ToF image to a localised search range for dense stereo, a global optimisation strategy is adopted for producing smooth results that preserve discontinuity. They also use edges of colour and disparity images for automatic leaf detection and develop a smoothing method necessary for accurately estimating surface area. In addition to show that combining stereo and ToF images gives superior qualitative and quantitative results, 149 automatic measurements on leaf area using the authors system in a validation trial have a correlation of 0.97 with true values and the root-mean-square error is 10.97 cm2, which is 9.3% of the average leaf area. Their approach could potentially be applied for combining other modalities of images with large difference in image resolutions and camera positions.
Keywords
greenhouses; image resolution; image sensors; optimisation; smoothing methods; stereo image processing; surface topography measurement; ToF imaging; automatic leaf detection; camera position; depth information transformation; global optimisation strategy; greenhouse environment; image disparity; image resolution; nondestructive automatic leaf area measurement; root-mean-square error; smoothing method; stereo imaging; surface area measurement; time-of-flight imaging;
fLanguage
English
Journal_Title
Computer Vision, IET
Publisher
iet
ISSN
1751-9632
Type
jour
DOI
10.1049/iet-cvi.2013.0056
Filename
6900075
Link To Document