查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting originatin g from Washington, D.C., by NewsRx journalists, a patent application by the inve ntors Boyle, Kevin (San Francisco, CA, US); Konrad, Robert (San Francisco, CA, U S); Padmanaban, Nitish (Menlo Park, CA, US), filed on November 15, 2022, was mad e available online on May 16, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “Eye-tracking systems capture images of the eyes in order to d etermine the 3D gaze of the user, or a 2D projection of that gaze onto a surface or plane, such as a screen or typical viewing distance. This is done either thr ough a computer vision segmentation of the image of the eye into various parts, i.e. pupil, sclera, iris, eye lids, canthus, etc., the features of which are the n exported as parameters that can be used to calculate the user’s gaze based on calibration data or generate an eye model for the same purpose, or the eye image s are fed directly into a neural network or other machine learning approach that infers the segmentation and/or user’s gaze directly from the images based on a database of labeled eye images. The parameters extracted from a traditional comp uter vision approach can also be used with a machine learning approach, with or without the images of the eyes, which may also be scaled to various lower resolu tions. In all cases, the quality of the images, with respect to contrast, lighti ng, sensitivity, etc., and the amount of computation required to extract the fea tures of the eye or infer the gaze directly from the images is of first importan ce to the robustness and quality of the gaze estimate. This is especially true i n a head-mounted, mobile system intended to operate both indoors and outdoors, i n uncontrolled and variable lighting conditions. The complexity of extracting in formation from the eye images, especially the crucial pupil position, requires h igh complexity in the computer vision algorithms used for the task, and robustne ss to environmental effects on those images is the main challenge remaining for eye-tracking systems.”
NETL
NSTL