Usmani Kashif, Krishnan Gokul, O'Connor Timothy, Javidi Bahram
Opt Express. 2021 Apr 12;29(8):12215-12228. doi: 10.1364/OE.421287.
Polarimetric imaging is useful for object recognition and material classification because of its ability to discriminate objects based on polarimetric signatures of materials. Polarimetric imaging of an object captures important physical properties such as shape and surface properties and can be effective even in low light environments. Integral imaging is a passive three-dimensional (3D) imaging approach that takes advantage of multiple 2D imaging perspectives to perform 3D reconstruction. In this paper, we propose a unified polarimetric detection and classification of objects in degraded environments such as low light and the presence of occlusion. This task is accomplished using a deep learning model for 3D polarimetric integral imaging data captured in the visible spectral domain. The neural network system is designed and trained for 3D object detection and classification using polarimetric integral images. We compare the detection and classification results between polarimetric and non-polarimetric 2D and 3D imaging. The system performance in degraded environmental conditions is evaluated using average miss rate, average precision, and F-1 score. The results indicate that for the experiments we have performed, polarimetric 3D integral imaging outperforms 2D polarimetric imaging as well as non-polarimetric 2D and 3D imaging for object recognition in adverse conditions such as low light and occlusions. To the best of our knowledge, this is the first report for polarimetric 3D object recognition in low light environments and occlusions using a deep learning-based integral imaging. The proposed approach is attractive because low light polarimetric object recognition in the visible spectral band benefits from much higher spatial resolution, more compact optics, and lower system cost compared with long wave infrared imaging which is the conventional imaging approach for low light environments.
偏振成像对于目标识别和材料分类很有用，因为它能够基于材料的偏振特征来区分目标。对物体进行偏振成像可以捕捉到诸如形状和表面特性等重要物理属性，并且即使在低光照环境下也能有效。积分成像是一种被动三维（3D）成像方法，它利用多个二维成像视角来进行三维重建。在本文中，我们提出了一种在诸如低光照和存在遮挡等退化环境中对物体进行统一的偏振检测和分类方法。这项任务是通过对在可见光谱域中捕获的三维偏振积分成像数据使用深度学习模型来完成的。该神经网络系统被设计并训练用于使用偏振积分图像进行三维目标检测和分类。我们比较了偏振和非偏振二维及三维成像之间的检测和分类结果。使用平均漏检率、平均精度和F1分数来评估该系统在退化环境条件下的性能。结果表明，对于我们所进行的实验，在诸如低光照和遮挡等不利条件下进行目标识别时，偏振三维积分成像优于二维偏振成像以及非偏振二维和三维成像。据我们所知，这是关于在低光照环境和存在遮挡情况下使用基于深度学习的积分成像进行偏振三维目标识别的首份报告。所提出的方法具有吸引力，因为与用于低光照环境的传统成像方法——长波红外成像相比，在可见光谱带中进行低光照偏振目标识别受益于更高的空间分辨率、更紧凑的光学器件以及更低的系统成本。
