Abstract: A head mounted device includes a light sensor configured to generate light data in response to measuring scene light in an external environment of the head mounted device, a display configured to present a virtual image to an eyebox area of the head mounted device, a near-eye dimming element configured to modulate a transmission of the scene light to the eyebox area in response to a transmission command, and processing logic configured to adjust the transmission command of the dimming element in response to a brightness level of the virtual image and the light data generated by the light sensor.

Abstract: A camera system. The camera system includes a sensor array; an infrared (IR) illumination system configured to emit active IR light in an IR light sub-band; a spectral illumination system configured to emit active spectral light in a spectral light sub-band; one or more logic machines; and one or more storage machines. The storage machines hold instructions executable by the one or more logic machines to address the sensor array to acquire an ambient image; based on at least the ambient image, activate the IR illumination system and address the sensor array to acquire an actively-IR-illuminated depth image; and based on at least the actively-IR-illuminated depth image, activate the spectral illumination system and address the sensor array to acquire an actively-spectrally-illuminated spectral image.

Abstract: A camera system is configured to automatically monitor an area. Depth image(s) of the area are acquired based on active IR light emitted by the camera system and reflected from the area to a sensor array of the camera system. The depth image(s) are computer analyzed to identify a human subject. For each spectral illuminator of the camera system, spectral light image(s) of the area are acquired based on active spectral light in the spectral light sub-band of the spectral illuminator reflected from the area to the sensor array. The spectral light image(s) for the spectral illuminators are computer analyzed to identify an interaction between the human subject and an object in the area. In response to identifying the interaction between the human subject and the object in the area, an action to be performed for the object in the area is computer issued.

Abstract: A camera system includes one or more spectral illuminators, a tunable optical filter, and a sensor array. Active spectral light emitted from the one or more spectral illuminators towards a scene is dynamically tuned to an illumination sub-band selected from a plurality of different illumination sub-bands. Sequentially for each of a plurality of fluorescing light sub-bands different than the selected illumination sub-band, the tunable optical filter is adjusted to block light from being transmitted from the scene to the sensor array in all but a tested fluorescing light sub-band from the plurality of different fluorescing light sub-bands, and the sensor array is addressed to acquire one or more image of the scene in the tested fluorescing light sub-band.

Abstract: A camera includes a time-of-flight illuminator configured to emit active IR light and a plurality of spectral illuminators, each spectral illuminator configured to emit active spectral light in a different spectral light sub-band. The camera further includes a sensor array that includes a plurality of differential sensors. Each differential sensor is configured to differentially measure both 1) the active IR light, and 2) the active spectral light in each of the different spectral light sub-bands. The camera further includes an output machine operatively connected to the sensor array. The output machine is configured to output a matrix of pixels based on time-multiplexed measurements of the sensor array. Each pixel of the matrix includes 1) a depth value, and 2) a plurality of spectral values. Each of the plurality of spectral values corresponds to a spectral light sub-band of one of the plurality of spectral illuminators.

Abstract: A method to identify one or more depth-image segments that correspond to a predetermined object type is enacted in a depth-imaging controller operatively coupled to an optical time-of-flight (ToF) camera; it comprises: receiving depth-image data from the optical ToF camera, the depth-image data exhibiting an aliasing uncertainty, such that a coordinate (X, Y) of the depth-image data maps to a periodic series of depth values {Zk}; and labeling, as corresponding to the object type, one or more coordinates of the depth-image data exhibiting the aliasing uncertainty.

Abstract: A camera is configured to output a test depth+multi-spectral image including a plurality of pixels. Each pixel corresponds to one of the plurality of sensors of a sensor array of the camera and includes at least a depth value and a spectral value for each spectral light sub-band of a plurality of spectral illuminators of the camera. A face recognition machine is previously trained with a set of labeled training depth+multi-spectral images having a same structure as the test depth+multi-spectral image. The face recognition machine is configured to output a confidence value indicating a likelihood that the test depth+multi-spectral image includes a face.

Abstract: A camera system includes one or more spectral illuminators, a tunable optical filter, and a sensor array. Active spectral light emitted from the one or more spectral illuminators towards a scene is dynamically tuned to an illumination sub-band selected from a plurality of different illumination sub-bands. Sequentially for each of a plurality of fluorescing light sub-bands different than the selected illumination sub-band, the tunable optical filter is adjusted to block light from being transmitted from the scene to the sensor array in all but a tested fluorescing light sub-band from the plurality of different fluorescing light sub-bands, and the sensor array is addressed to acquire one or more image of the scene in the tested fluorescing light sub-band.

Abstract: A camera system is configured to automatically monitor an area. Depth image(s) of the area are acquired based on active IR light emitted by the camera system and reflected from the area to a sensor array of the camera system. The depth image(s) are computer analyzed to identify a human subject. For each spectral illuminator of the camera system, spectral light image(s) of the area are acquired based on active spectral light in the spectral light sub-band of the spectral illuminator reflected from the area to the sensor array. The spectral light image(s) for the spectral illuminators are computer analyzed to identify an interaction between the human subject and an object in the area. In response to identifying the interaction between the human subject and the object in the area, an action to be performed for the object in the area is computer issued.

Abstract: A method to identify one or more depth-image segments that correspond to a predetermined object type is enacted in a depth-imaging controller operatively coupled to an optical time-of-flight (ToF) camera; it comprises: receiving depth-image data from the optical ToF camera, the depth-image data exhibiting an aliasing uncertainty, such that a coordinate (X, Y) of the depth-image data maps to a periodic series of depth values {Zk}; and labeling, as corresponding to the object type, one or more coordinates of the depth-image data exhibiting the aliasing uncertainty.

Abstract: Examples are disclosed that relate to methods and systems for determining if a fluid is present on a surface. In one example, a method comprises illuminating the surface with narrow-band light and using an image sensor comprising a narrow-bandpass filter matching the bandwidth of the narrow-band light to obtain a first image of the surface. A second image of the surface with the narrow-band light deactivated is obtained. A third image is generated by subtracting the second image from the first image. The third image is thresholded, one or more contrasting regions are detected, and the presence of fluid on the surface is determined.

Abstract: A camera is configured to output a test depth+multi-spectral image including a plurality of pixels. Each pixel corresponds to one of the plurality of sensors of a sensor array of the camera and includes at least a depth value and a spectral value for each spectral light sub-band of a plurality of spectral illuminators of the camera. A face recognition machine is previously trained with a set of labeled training depth+multi-spectral images having a same structure as the test depth+multi-spectral image. The face recognition machine is configured to output a confidence value indicating a likelihood that the test depth+multi-spectral image includes a face.

Abstract: A camera includes a time-of-flight illuminator configured to emit active IR light and a plurality of spectral illuminators, each spectral illuminator configured to emit active spectral light in a different spectral light sub-band. The camera further includes a sensor array that includes a plurality of differential sensors. Each differential sensor is configured to differentially measure both 1) the active IR light, and 2) the active spectral light in each of the different spectral light sub-bands. The camera further includes an output machine operatively connected to the sensor array. The output machine is configured to output a matrix of pixels based on time-multiplexed measurements of the sensor array. Each pixel of the matrix includes 1) a depth value, and 2) a plurality of spectral values. Each of the plurality of spectral values corresponds to a spectral light sub-band of one of the plurality of spectral illuminators.

Abstract: A camera is configured to output a test depth+multi-spectral image including a plurality of pixels. Each pixel corresponds to one of the plurality of sensors of a sensor array of the camera and includes at least a depth value and a spectral value for each spectral light sub-band of a plurality of spectral illuminators of the camera. An object recognition machine is previously trained with a set of labeled training depth+multi-spectral images having a same structure as the test depth+multi-spectral image. The object recognition machine is configured to output a confidence value indicating a likelihood that the test depth+multi-spectral image includes a specified object.