Showing papers by "Kenji Omasa published in 2020"
TL;DR: The optimal shooting angles on the MCP based SfM system developed are determined and it is found that it is better in terms of computational time and accuracy to merge partial 3D models from images taken at each appropriate VZA, then construct complete 3D model (Method 1), rather than to construct3D model by using image taken at all appropriate VZAs (Method 2).
Abstract: Measurement of plant structure is useful in monitoring plant conditions and understanding the responses of plants to environmental changes. 3D imaging technologies, especially the passive-SfM (Structure from Motion) algorithm combined with a multi-camera photography (MCP) system has been studied to measure plant structure due to its low-cost, close-range, and rapid image capturing ability. However, reconstruction of 3D plant models with complex structure is a time-consuming process and some systems have failed to reconstruct 3D models properly. Therefore, an MCP based SfM system was developed and an appropriate reconstruction method and optimal range of camera-shooting angles were investigated. An MCP system which utilized 10 cameras and a rotary table for plant was developed. The 3D mesh model of a single leaf reconstruction using a set of images taken at each viewing zenith angle (VZA) from 12° (C2 camera) to 60° (C6 camera) by the MCP based SfM system had less undetected or unstable regions in comparison with other VZAs. The 3D mesh model of a whole plant, which merged 3D dense point cloud models built from a set of images taken at each appropriate VZA (Method 1), had high accuracy. The Method 1 error percentages for leaf area, leaf length, leaf width, stem height, and stem width are in the range of 2.6–4.4%, 0.2–2.2%, 1.0–4.9%, 1.9–2.8%, and 2.6–5.7% respectively. Also, the error of the leaf inclination angle was less than 5°. Conversely, the 3D mesh model of a whole plant built directly from a set of images taken at all appropriate VZAs (Method 2) had lower accuracy than that of Method 1. For Method 2, the error percentages of leaf area, leaf length, and leaf width are in the range of 3.1–13.3%, 0.4–3.3%, and 1.6–8.6%, respectively. It was difficult to obtain the error percentages of stem height and stem width because some information was missing in this model. In addition, the calculation time for Method 2 was 1.97 times longer computational time in comparison to Method 1. In this study, we determined the optimal shooting angles on the MCP based SfM system developed. We found that it is better in terms of computational time and accuracy to merge partial 3D models from images taken at each appropriate VZA, then construct complete 3D model (Method 1), rather than to construct 3D model by using images taken at all appropriate VZAs (Method 2). This is because utilization of incorporation of incomplete images to match feature points could result in reduced accuracy in 3D models and the increase in computational time for 3D model reconstruction.
9 citations
TL;DR: Results provide evidence that the IpRFlab and IpRFfield taken from polarimetric measurements can be considered as the proxy of photometric measurements (BRF and HDRF) in both laboratory and field but also open the possibility to improve the accuracy of LCC estimation using a nonpolarized spectral reflectance factor from multiangular polarIMetric measurements.
Abstract: Optical properties of light reflected from leaves can be described by both intensity and polarization, however, most studies focused on the intensity in the estimation of plant leaf biochemical parameters In this study, multiangular photometric and polarimetric measurements of leaves from three different plant species are first performed in laboratory to estimate leaf chlorophyll content (LCC) using spectral indices at different viewing zenith angles Based on the Stokes parameters, the spectral indices in terms of the I parameter reflectance factors measured in laboratory (IpRFlab if polarizer extinction is considered) can be used to estimate LCC, which has a similar accuracy as bidirectional reflectance factor (BRF); and the nonpolarized spectral proportion [the reduction of bidirectional polarized reflectance factor (BPRF) from IpRF (IpRF-BPRF)] improves the ability of the spectral indices, including single wavelength, simple ratio, simple difference and normalized difference indices, along with some other indices to estimate LCC using multiangular measurements Subsequently, the field photometric and polarimetric measurements of leaves further confirm that the nonpolarized proportion improves the estimation of LCC for some spectral indices These results not only provide evidence that the IpRFlab and IpRFfield taken from polarimetric measurements can be considered as the proxy of photometric measurements (BRF and HDRF) in both laboratory and field but also open the possibility to improve the accuracy of LCC estimation using a nonpolarized spectral reflectance factor from multiangular polarimetric measurements These findings indicate that polarized remote sensing may play a significant role in vegetation studies
4 citations