Fast estimation of optical properties of pear using a single snapshot technique combined with a least-squares support vector regression model based on spatial frequency domain imaging

He Xueming; Li Tingwei; Fu Xiaping; Jiang Xu; Gao Yingwang; Rao Xiuqin

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Fast estimation of optical properties of pear using a single snapshot technique combined with a least-squares support vector regression model based on spatial frequency domain imaging

机译：使用单快照技术与基于空间域成像的最小二乘支持向量回归模型结合使用单一快照技术的梨光学性质的快速估计

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Spatial frequency domain imaging has great potential in agricultural produce quality control due to its advantage of wide-field mapping of absorption (mu(a)) and reduced scattering (mu(s)') parameters. However, it is not widely adopted in real applications due to the large time cost during image acquisition and inversion calculation processes. In this study, a single snapshot technique was used to obtain ac and dc components (R-d_ac, R-d _dc) of diffuse reflectance of turbid media (phantoms and pears). The validation results for the snapshot method indicate that at the spatial frequency of 1000/3 m(-1), it achieved the optimal demodulation, by comparison with the results obtained by the commonly used time-domain amplitude demodulation method. Diffusion approximation, artificial neural network, least-squares support vector machine regression (LSSVR), and LSSVR combined with a genetic algorithm (LSSVR + GA) were then used to predict mu(a) and mu(s)' from the obtained R-d_ac, R-d_dc at the f(x) of 1000/3 m(-1). Validation results indicated that the LSSVR method took the least time to calculate mu(a) and mu(s)' with high performance. The proposed imaging system and algorithm were implemented for the inspection of a pear bruise. Results indicated that the bruise, which is not obviously distinguishable in original gray maps, can show obvious contrast in calculated mu(a) and mu(s)' maps, especially mu(a )maps. Further, the contrast becomes more obvious with the passage of time. In summary, this study developed a low-cost spatial frequency imaging system and matching software that could realize fast detection of optical properties for a pear with the proposed snapshot and LSSVR algorithms. (C) 2019 Optical Society of America

机译：由于其优点是吸收的宽场映射（MU（a））和减少散射（MU（S））参数，空间频域成像具有很大的潜力。然而，由于图像采集和反演计算过程期间的时间成本较大，因此在实际应用中不广泛采用。在该研究中，使用单一快照技术来获得浑浊介质（幽灵和梨）的漫反射率的AC和DC组分（R-D_AC，R-D _dc）。快照方法的验证结果表明，在1000/3 m（-1）的空间频率下，它通过与通过常用的时域幅度解调方法获得的结果进行比较实现了最佳解调。扩散近似，人工神经网络，最小二乘支持向量机回归（LSSVR）和LSSVR与遗传算法（LSSVR + GA）结合使用，用于预测所获得的R-的MU（A）和MU（S）' d_ac，f（x）为1000/3 m（-1）的r-d_dc。验证结果表明，LSSVR方法采用了高性能的MU（A）和MU（S）的时间最短的时间。所提出的成像系统和算法用于检查梨瘀伤的检查。结果表明，在原始灰色地图中没有明显可区分的瘀伤，可以在计算的MU（A）和MU（S）的地图中显示出明显的对比度，尤其是MU（A）地图。此外，随着时间的推移，对比度变得更加明显。总之，本研究开发出低成本的空间频率成像系统和匹配的软件，可以实现具有所提出的快照和LSSVR算法的梨的光学性质的快速检测。（c）2019年光学学会

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《Applied optics》 |2019年第15期|共10页
作者
He Xueming; Li Tingwei; Fu Xiaping; Jiang Xu; Gao Yingwang; Rao Xiuqin;
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作者单位

Zhejiang Univ Coll Biosyst Engn &

Food Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China;

Zhejiang Univ Coll Biosyst Engn &

Food Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China;

Zhejiang Sci Tech Univ Fac Mech Engn &

Automat Xiasha Higher Educ Zone 928 Second Ave Hangzhou 310018 Zhejiang Peoples R China;

Zhejiang Sci Tech Univ Fac Mech Engn &

Automat Xiasha Higher Educ Zone 928 Second Ave Hangzhou 310018 Zhejiang Peoples R China;

Zhejiang Univ Coll Biosyst Engn &

Food Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China;

Zhejiang Univ Coll Biosyst Engn &

Food Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China;

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Fast estimation of optical properties of pear using a single snapshot technique combined with a least-squares support vector regression model based on spatial frequency domain imaging

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