Magic Leap, Inc.

Steven L Brunton

Brian T Schowengerdt

Eric J Seibel

Chen Gong

Laura Cristina Trutoiu

Nils Benjamin Erichson

Techniques for tracking eye movement in an augmented reality system identify a plurality of base images of an object or a portion thereof. A search image may be generated based at least in part upon at least some of the plurality of base images. A deep learning result may be generated at least by performing a deep learning process on a base image using a neural network in a deep learning mode. A captured image may be localized at least by performing an image registration process on the captured image and the search image using a Kalman filter model and the deep learning result.

Eye tracking in near-eye displays

A retina image template matching method is based on the registration and comparison between the images captured with portable low-cost fundus cameras (e.g., a consumer grade camera typically incorporated into a smartphone or tablet computer) and a baseline image. The method solves the challenges posed by registering small and low-quality retinal template images captured with such cameras. Our method combines dimension reduction methods with a mutual information (MI) based image registration technique. In particular, principle components analysis (PCA) and optionally block PCA are used as a dimension reduction method to localize the template image coarsely to the baseline image, then the resulting displacement parameters are used to initialize the MI metric optimization for registration of the template image with the closest region of the baseline image.

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Seattle, WA, United States of America

Steven L Brunton