Open AccessNon‐destructive automatic leaf area measurements by combining stereo and time‐of‐flight images
Author(s) -
Song Yu,
Glasbey Chris A.,
Polder Gerrit,
Heijden Gerie W.A.M.
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0056
Subject(s) - artificial intelligence , computer vision , computer science , smoothing , stereo imaging , discontinuity (linguistics) , focus (optics) , mean squared error , remote sensing , mathematics , geology , optics , mathematical analysis , statistics , physics
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 cm 2 , 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.