Abstract: Methods and devices are described for image or video capture while in a manual mode. In some aspects, a device includes one or more processors. The device also includes a memory coupled to the one or more processors. The memory includes instructions that, when executed by the one or more processors, causes the device to generate a first image frame of a scene using manual mode settings and generate a second image frame of the scene using automatic mode settings different from the manual mode settings.

Abstract: A method and apparatus facilitate securing access to applications. An apparatus includes a touch-sensitive display configured to display a plurality of objects representing a plurality of applications, the touch-sensitive display configured to receive a selection of a first object of the plurality of objects representing a first application of the plurality of applications. The apparatus also includes an authentication module coupled to the display. The authentication module is configured to receive biometric data in response to the selection of the first object, compare the received biometric data to a biometric template, and generate a match signal upon a determination that the received biometric data matches the biometric template. The apparatus also includes a processor configured to prevent access to the first application before the match signal is received, and enable access to the first application in response to the receipt of the match signal.

Abstract: A camera image sensor captures imaging pixel data and focus pixel data. The camera determines an imaging analog gain based on the imaging pixel data, and determines a focus analog gain based on the focus pixel data. When capturing the one or more subsequent frames, the image sensor applies the imaging analog gain to the imaging pixels and applies the focus analog gain to the focus pixels. Optionally, applying the focus analog gain to the focus pixels brings an average focus pixel luminance within a range, or brings a phase disparity confidence above a threshold.

Abstract: In multi-sensor devices (e.g., devices with multiple cameras, such as a dual camera device), it may be efficient to transition sensors from an active state or image capture state to a low power mode (LPM) to reduce device power consumption. When the multi-sensor device transitions from use of one sensor (e.g., a wide angle camera) to another sensor (e.g., a telescoping camera), the dual camera device may transition the sensor not being used to an LPM to reduce power consumption. As a sensor is transitioned out of the LPM (e.g., when the dual camera device zooms out of focus and intends to transition back to capturing a wide angle image), the sensor may load sensor setting configurations from in-sensor memory (e.g., flash memory) to reduce latency associated with initialization of sensor settings (e.g., to reduce latency associated with initialization of sensor settings from scratch or from default sensor settings).

Abstract: Some dynamic pixel binning methods may involve identifying ROI optical sensor data corresponding to optical sensor pixels in a region of interest (ROI). Some such methods also may involve identifying non-ROI optical sensor data corresponding to optical sensor pixels outside the region of interest and determining a first binning factor for the non-ROI optical sensor data. The first binning factor may be based on a number of pixels in the region of interest and a target output data rate. Some such methods also may involve applying the first binning factor to the non-ROI optical sensor data and outputting optical sensor data, including the binned non-ROI optical sensor data, at the target output data rate.

Abstract: A method of communication includes determining, at a mobile device, a speech quality metric for an incoming speech signal associated with a voice call. The speech quality metric is based on an environment of the mobile device. The method also includes converting incoming speech associated with the incoming speech signal to text in response to a determination that the speech quality metric fails to satisfy a speech quality metric threshold. The method further includes displaying the text at a display screen of the mobile device during the voice call.

Abstract: Aspects of the present disclosure relate to systems and methods for performing automatic white balance (AWB). An example device may include a memory and a processor coupled to the memory. The processor may be configured to receive a first image of a scene, measure a first illuminant of the first received image, compare the first illuminant and a first illuminant value, determine the scene is changing between the first received image and a previous image based on the comparison, adjust a first AWB convergence rate to a second AWB convergence rate in response to determining the scene is changing, and converge from a first balancing factor to a second balancing factor for one or more white balance operations based on the second AWB convergence rate.

Abstract: Methods, systems, and devices for image processing are described. The method includes using exposure control processes to set an exposure length for a set of one or more image pixels of a sensor, determining a light level of an environment of the sensor and a confidence level associated with a set of one or more autofocus pixels of the sensor, selecting one of a first exposure control process or a second exposure control process for the set of one or more autofocus pixels of the sensor, where the selection is based on the light level and the confidence level, performing an autofocus operation for the sensor based on an output of the one or more autofocus pixels and the selected one of the first or the second exposure control process, and outputting image data from the one or more image pixels of the sensor based on the autofocus operation.

Abstract: Some dynamic pixel binning methods may involve identifying ROI optical sensor data corresponding to optical sensor pixels in a region of interest (ROI). Some such methods also may involve identifying non-ROI optical sensor data corresponding to optical sensor pixels outside the region of interest and determining a first binning factor for the non-ROI optical sensor data. The first binning factor may be based on a number of pixels in the region of interest and a target output data rate. Some such methods also may involve applying the first binning factor to the non-ROI optical sensor data and outputting optical sensor data, including the binned non-ROI optical sensor data, at the target output data rate.

Abstract: Methods, systems, and devices for dual phase detection auto focus (PDAF) power optimization are described. A camera device may capture a frame including a pixel array representing a scene. In some examples, each pixel of the pixel array may be a phase detection (PD) pixel having one or more values, or one or more PD pixels positioned randomly across the pixel array. The camera device may identify a configuration of the pixel array, and determine a condition of the pixel array relative to the configuration. The configuration may be a binning configuration or a frame pattern configuration, and the condition may include an illumination condition related to the pixel array representing the scene. The camera device may determine and apply a reconfiguration to at least a portion of the pixel array based on the condition of the pixel array, and determine a lens position of the camera device therewith.

Abstract: In one implementation, an electronic visual-rendering device includes an eye-tracking sensor and at least a first component. The eye-tracking sensor is configured to detect an eye-close event and, in response, output an eye-close-event message. The first component is configured to operate in at least a normal-power mode and a first low-power mode. The first components is configured to transition from operating in the normal-power mode to operating in the first low-power mode in response to the eye-tracking sensor's output of the eye-close-event message.

Abstract: Methods, systems, and apparatuses are provide to perform automatic white balancing. For example, the methods receive, from a plurality of sensing elements in a sensor array, first image data corresponding to an image of a target scene that includes a human face. The methods also detect a region of the image that includes the human face, identify a portion of the first image data that corresponds to the detected region, and compute first gain values based on the identified portion of the first image data and reference image data that characterizes the human face. Further, the methods perform an automatic white balancing operation on the first image data based on the first gain values.

Abstract: Methods, systems, and devices for gesture detection are described. A device may identify a set of configured gestures and determine a set of minimum frame rates, where each minimum frame rate may correspond to at least one configured gesture in the set of configured gestures. The device may set an initial frame rate for a sensor based at least in part on the set of minimum frame rates. For example, the device may identify a maximum frame rate of the set of minimum frame rates and set the initial frame rate to be equal to or greater than the maximum frame rate. The device may capture a set of images using an initial frame rate and detect a configured gesture of the set of configured gestures based on the set of images.

Abstract: Methods, systems, and devices for dual phase detection auto focus (PDAF) power optimization are described. A camera device may capture a frame including a pixel array representing a scene. In some examples, each pixel of the pixel array may be a phase detection (PD) pixel having one or more values, or one or more PD pixels positioned randomly across the pixel array. The camera device may identify a configuration of the pixel array, and determine a condition of the pixel array relative to the configuration. The configuration may be a binning configuration or a frame pattern configuration, and the condition may include an illumination condition related to the pixel array representing the scene. The camera device may determine and apply a reconfiguration to at least a portion of the pixel array based on the condition of the pixel array, and determine a lens position of the camera device therewith.

Abstract: Aspects of the present disclosure relate to systems and methods for performing automatic white balance (AWB). An example device may include a memory and a processor coupled to the memory. The processor may be configured to receive a first image of a scene, measure a first illuminant of the first received image, compare the first illuminant and a first illuminant value, determine the scene is changing between the first received image and a previous image based on the comparison, adjust a first AWB convergence rate to a second AWB convergence rate in response to determining the scene is changing, and converge from a first balancing factor to a second balancing factor for one or more white balance operations based on the second AWB convergence rate.

Abstract: Among other things, the present disclosure relates to image searching. With a depth map, image pixels of an image having depth values intersecting a desired optical field can be identified. A territory can be set based on locations of the identified image pixels. Feature detection can be performed on the image within the set territory. The feature detection can be limited to the set territory.

Abstract: Methods and devices are described for image or video capture while in a manual mode. In some aspects, a device includes one or more processors. The device also includes a memory coupled to the one or more processors. The memory includes instructions that, when executed by the one or more processors, causes the device to generate a first image frame of a scene using manual mode settings and generate a second image frame of the scene using automatic mode settings different from the manual mode settings.