摘要
以下引文是新闻编辑从新闻编辑提供的背景资料中获得的发明人：“眼睛跟踪系统捕获眼睛的图像，以确定眼睛的三维注视。”用户，或该凝视在表面或平面上的2D投影，例如屏幕或典型的观看距离。这是通过计算机视觉将眼睛的图像分割成不同的部分，一.e .瞳孔、巩膜、虹膜、眼睑、眼角等，其特征随后作为参数导出，可用于基于校准数据计算用户的注视，或为其生成眼睛模型或者眼睛图像直接输入神经网络或其他机器学习方法，基于标记眼数据库直接从imag es中推断分割和/或用户注视图像。从传统的计算机视觉方法中提取的参数也可以用于机器学习每一种方法,不管有没有眼睛的图像,也可以根据不同的图像进行缩放分辨率较低。在所有情况下，图像的质量，关于CO ntrast、照明、灵敏度等，以及绘制眼睛特征或直接从中推断注视所需的计算量图像对视线估计的鲁棒性和质量至关重要。这尤其这适用于头戴式移动系统，目的是在室内和室外，在不受控制的情况下运行以及可变照明条件。从眼睛图像中提取信息的复杂性，特别是关键的瞳孔位置，要求用于该任务的计算机视觉算法具有很高的复杂性，而对环境影响的鲁棒性是眼动跟踪的主要挑战系统。
Abstract
The following quote was obtained by the news editors from the background informa tion supplied bythe inventors: “Eye-tracking systems capture images of the eyes in order to determine the 3D gaze of theuser, or a 2D projection of that gaze onto a surface or plane, such as a screen or typical viewing distance.This is d one either through a computer vision segmentation of the image of the eye into v arious parts,i.e. pupil, sclera, iris, eye lids, canthus, etc., the features of which are then exported as parameters thatcan be used to calculate the user’s gaze based on calibration data or generate an eye model for the samepurpose, or the eye images are fed directly into a neural network or other machine learning approach thatinfers the segmentation and/or user’s gaze directly from the imag es based on a database of labeled eyeimages. The parameters extracted from a tr aditional computer vision approach can also be used with amachine learning appr oach, with or without the images of the eyes, which may also be scaled to variou slower resolutions. In all cases, the quality of the images, with respect to co ntrast, lighting, sensitivity, etc.,and the amount of computation required to e xtract the features of the eye or infer the gaze directly fromthe images is of first importance to the robustness and quality of the gaze estimate. This is esp eciallytrue in a head-mounted, mobile system intended to operate both indoors a nd outdoors, in uncontrolledand variable lighting conditions. The complexity of extracting information from the eye images, especiallythe crucial pupil positi on, requires high complexity in the computer vision algorithms used for the task ,and robustness to environmental effects on those images is the main challenge remaining for eye-trackingsystems.”
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