The subject matter described herein includes methods, systems, and computer readable media for automated attention assessment. According to one method, a method for automated attention assessment includes obtaining head and iris positions of a user using a camera while the user watches a display screen displaying a video containing dynamic region-based stimuli designed for identifying a neurodevelopmental and/or psychiatric (neurodevelopmental/psychiatric) disorder; analyzing the head and iris positions of the user to detect attention assessment information associated with the user, wherein the attention assessment information indicates how often and/or how long the user attended to one or more regions of the display screen while watching the video; determining that the attention assessment information is indicative of the neurodevelopmental/psychiatric disorder; and providing, via a communications interface, the attention assessment information, a diagnosis, or related data.

The subject matter described herein includes methods, systems, and computer readable media for automated attention assessment. According to one method, a method for automated attention assessment includes obtaining head and iris positions of a user using a camera while the user watches a display screen displaying a video containing dynamic region-based stimuli designed for identifying a neurodevelopmental and/or psychiatric (neurodevelopmental/psychiatric) disorder; analyzing the head and iris positions of the user to detect attention assessment information associated with the user, wherein the attention assessment information indicates how often and/or how long the user attended to one or more regions of the display screen while watching the video; determining that the attention assessment information is indicative of the neurodevelopmental/psychiatric disorder; and providing, via a communications interface, the attention assessment information, a diagnosis, or related data.

A vehicle computing system may implement techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques may include determining a feature with respect to a detected objects (e.g., likelihood that the detected object will impact operation of the vehicle) and/or a location of the vehicle and determining based on the feature a model to use to predict behavior (e.g., estimated states) of proximate objects (e.g., the detected object). The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict the behavior. Different models may be used to predict behavior of different objects and/or regions in the environment. Each model may receive sensor data as an input, and output predicted behavior for the detected object. Based on the predicted behavior of the object, a vehicle computing system may control operation of the vehicle.

Embodiments of the present invention provide a display panel, including a display region, wherein the display region includes an iris recognition region; the display panel further includes a light conversion layer disposed in the iris recognition region, the light conversion layer is configured to convert visible light incident on the light conversion layer into infrared light, and the infrared light is emitted from a display side of the display panel.

A method and apparatus for waking up a device, an electronic device, and a storage medium are provided, which are related to fields of image processing and deep learning. The method includes: acquiring an environment image of a surrounding environment of a target device in real time, and recognizing a face region of a user in the environment image; acquiring a plurality of facial landmarks in the face region, and acquiring a left eye image and a right eye image according to the facial landmarks; acquiring a left eye sight classification result and a right eye sight classification result according to the left eye image and the right eye image; and waking up the target device in a case of determining that the user is looking at the target device according to the left eye sight classification result and the right eye sight classification result.

Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.

Disclosed are a fatigue state detection method and apparatus, a medium and a device. The method includes: obtaining image blocks containing an organ area of a target object from a plurality of video frames collected by a camera apparatus disposed in a mobile device, to obtain an image-block sequence that is based on the organ area; determining a fatigue state type of the target object based on the image-block sequence of the organ area; sending the image-block sequence to a cloud server if the fatigue state type meets a first preset type, and rendering the cloud server to detect a fatigue level of the target object based on the image-block sequence; and receiving fatigue level information about the target object that is returned by the cloud server. The present disclosure may improve accuracy of fatigue state detection, thereby helping to improve driving safety of the mobile device.

A visual perception device has a look-up table stored in a laser driver chip. The look-up table includes relational gain data to compensate for brighter areas of a laser pattern wherein pixels are located more closely than areas where the pixels are further apart and to compensate for differences in intensity of individual pixels when the intensities of pixels are altered due to design characteristics of an eye piece.

A visual perception device has a look-up table stored in a laser driver chip. The look-up table includes relational gain data to compensate for brighter areas of a laser pattern wherein pixels are located more closely than areas where the pixels are further apart and to compensate for differences in intensity of individual pixels when the intensities of pixels are altered due to design characteristics of an eye piece.

Methods and systems for determining an individual gaze value are disclosed herein. An exemplary method involves: (a) receiving gaze data for a first wearable computing device, wherein the gaze data is indicative of a wearer-view associated with the first wearable computing device, and wherein the first wearable computing device is associated with a first user-account; (b) analyzing the gaze data from the first wearable computing device to detect one or more occurrences of one or more advertisement spaces in the gaze data; (c) based at least in part on the one or more detected advertisement-space occurrences, determining an individual gaze value for the first user-account; and (d) sending a gaze-value indication, wherein the gaze-value indication indicates the individual gaze value for the first user-account.