Provided is a method and apparatus for eye tracking. An eye tracking method includes detecting an eye area corresponding to an eye of a user in a first frame of an image; determining an attribute of the eye area; selecting an eye tracker from a plurality of different eye trackers, the eye tracker corresponding to the determined attribute of the eye area; and tracking the eye of the user in a second frame of the image based on the selected eye tracker, the second frame being subsequent to the first frame.

A method for displaying virtual content to a user, the method includes determining an accommodation of the user's eyes. The method also includes delivering, through a first waveguide of a stack of waveguides, light rays having a first wavefront curvature based at least in part on the determined accommodation, wherein the first wavefront curvature corresponds to a focal distance of the determined accommodation. The method further includes delivering, through a second waveguide of the stack of waveguides, light rays having a second wavefront curvature, the second wavefront curvature associated with a predetermined margin of the focal distance of the determined accommodation.

There is provided mechanisms for calibration of an eye tracking system. An eye tracking system comprises a pupil centre corneal reflection (PCCR) based eye tracker and a non-PCCR based eye tracker. A method comprises obtaining at least one first eye position of a subject by applying the PCCR based eye tracker on an image set depicting the subject. The method comprises calibrating a head model of the non-PCCR based eye tracker, as applied on the image set, for the subject using the obtained at least one first eye position from the PCCR based eye tracker as ground truth. The head model comprises facial features that include at least one second eye position. The calibrating involves positioning the head model in order for its at least one second eye position to be consistent with the at least one first eye position given by the PCCR based eye tracker.

There is provided mechanisms for calibration of an eye tracking system. An eye tracking system comprises a pupil centre corneal reflection (PCCR) based eye tracker and a non-PCCR based eye tracker. A method comprises obtaining at least one first eye position of a subject by applying the PCCR based eye tracker on an image set depicting the subject. The method comprises calibrating a head model of the non-PCCR based eye tracker, as applied on the image set, for the subject using the obtained at least one first eye position from the PCCR based eye tracker as ground truth. The head model comprises facial features that include at least one second eye position. The calibrating involves positioning the head model in order for its at least one second eye position to be consistent with the at least one first eye position given by the PCCR based eye tracker.

An eye monitoring system is included in a headset of a virtual reality system or of an augmented reality system. The eye monitoring system determines distances between the eye monitoring system and portions of a user's eye enclosed by the headset. The eye monitoring system projects a temporally periodic pattern of light onto the user's eye via a sensor. The eye monitoring system determines a distance between the eye monitoring system and locations of the user's eye based on a phase shift of the periodic pattern of light captured by each pixel of the sensor. From the determined distances, the eye monitoring system determines features of the user's eye.

An eye monitoring system is included in a headset of a virtual reality system or of an augmented reality system. The eye monitoring system determines distances between the eye monitoring system and portions of a user's eye enclosed by the headset. The eye monitoring system projects a temporally periodic pattern of light onto the user's eye via a sensor. The eye monitoring system determines a distance between the eye monitoring system and locations of the user's eye based on a phase shift of the periodic pattern of light captured by each pixel of the sensor. From the determined distances, the eye monitoring system determines features of the user's eye.

In one example, an apparatus comprises: a first sensor layer, including an array of pixel cells configured to generate pixel data; and one or more semiconductor layers located beneath the first sensor layer with the one or more semiconductor layers being electrically connected to the first sensor layer via interconnects. The one or more semiconductor layers comprises on-chip compute circuits configured to receive the pixel data via the interconnects and process the pixel data, the on-chip compute circuits comprising: a machine learning (ML) model accelerator configured to implement a convolutional neural network (CNN) model to process the pixel data; a first memory to store coefficients of the CNN model and instruction codes; a second memory to store the pixel data of a frame; and a controller configured to execute the codes to control operations of the ML model accelerator, the first memory, and the second memory.

For measuring quality of view over a visual field of view of an eye, during a measuring period, deviations between gaze positions and associated stimulus positions where the stimulus to be followed was displayed when the gaze position was detected and magnitudes of the registered deviations arte determined. For each field portion of a map of a visual field of view, quality of view is determined in accordance with a quality of view estimates of associated ones of the registered deviations of which the associated stimulus positions are located relative to the gaze position so that the associated stimulus positions are in that field portion. For each of the associated ones of the registered deviations, the quality of view is estimated in accordance with the magnitude of that associated one of the registered deviations and with magnitudes of at least preceding or succeeding ones of the registered deviations.

A computer assisted surgery system comprising: a computerised surgical apparatus; and a control apparatus; wherein the control apparatus comprises circuitry configured to: receive information indicating a first region of a surgical scene from which information is obtained by the computerised surgical apparatus to make a decision; receive information indicating a second region of the surgical scene from which information is obtained by a medical professional to make a decision; determine if there is a discrepancy between the first and second regions of the surgical scene; and if there is a discrepancy between the first and second regions of the surgical scene: perform a predetermined process based on the discrepancy.

An image capturing apparatus acquires successive images in a time-series manner and performs image processing thereon. The image capturing apparatus detects a first region (for example, a face) of a subject from an image, and detects a second region (for example, a torso) of a subject from an image. The image capturing apparatus performs processing for searching for a detection result which is obtained from a current image with use of a detection result obtained from a previously acquired image and classifying detection results each satisfying a condition according to subject. In the search processing, identical-region search, which uses detection results of an identical region between the previously acquired image and the current image, is performed in preference to different-region search, which uses detection results of different regions between the previously acquired image and the current image.