Kowalski Bartlomiej, Huang Xiaojing, Steven Samuel, Dubra Alfredo
Department of Ophthalmology, Stanford University, Palo Alto, CA 94303, USA.
Institute of Optics, University of Rochester, Rochester, NY 14620, USA.
Biomed Opt Express. 2021 Sep 22;12(10):6496-6513. doi: 10.1364/BOE.433766. eCollection 2021 Oct 1.
An off-axis monocular pupil tracker designed for eventual integration in ophthalmoscopes for eye movement stabilization is described and demonstrated. The instrument consists of light-emitting diodes, a camera, a field-programmable gate array (FPGA) and a central processing unit (CPU). The raw camera image undergoes background subtraction, field-flattening, 1-dimensional low-pass filtering, thresholding and robust pupil edge detection on an FPGA pixel stream, followed by least-squares fitting of the pupil edge pixel coordinates to an ellipse in the CPU. Experimental data suggest that the proposed algorithms require raw images with a minimum of ∼32 gray levels to achieve sub-pixel pupil center accuracy. Tests with two different cameras operating at 575, 1250 and 5400 frames per second trained on a model pupil achieved 0.5-1.5 μm pupil center estimation precision with 0.6-2.1 ms combined image download, FPGA and CPU processing latency. Pupil tracking data from a fixating human subject show that the tracker operation only requires the adjustment of a single parameter, namely an image intensity threshold. The latency of the proposed pupil tracker is limited by camera download time (latency) and sensitivity (precision).
本文描述并展示了一种为最终集成到用于眼动稳定的检眼镜中而设计的离轴单眼瞳孔跟踪器。该仪器由发光二极管、相机、现场可编程门阵列（FPGA）和中央处理器（CPU）组成。原始相机图像在FPGA像素流上进行背景减法、场平坦化、一维低通滤波、阈值处理和鲁棒瞳孔边缘检测，然后在CPU中将瞳孔边缘像素坐标进行最小二乘拟合为椭圆。实验数据表明，所提出的算法需要至少约32灰度级的原始图像才能实现亚像素瞳孔中心精度。使用在模型瞳孔上训练的两台分别以每秒575帧、1250帧和5400帧运行的不同相机进行测试，在图像下载、FPGA和CPU处理延迟总计0.6 - 2.1毫秒的情况下，实现了0.5 - 1.5微米的瞳孔中心估计精度。来自注视人类受试者的瞳孔跟踪数据表明，该跟踪器操作仅需要调整一个参数，即图像强度阈值。所提出的瞳孔跟踪器的延迟受相机下载时间（延迟）和灵敏度（精度）限制。
