Abstract: The systems and/or processes described herein may calibrate an inertial measurement unit (IMU) of an electronic device in part by using images captured by one or more cameras of the electronic device. In this regard, an IMU of an electronic device may comprise a gyroscope, an accelerometer, a magnetometer, or any other type of motion sensor or rotational sensor.

Abstract: Various examples are directed to systems, devices, and methods effective to generate updated pixel values in a stitched image. A processor may be programmed to identify a first block of pixels located in a region of a stitched image. The region may include pixels representing parallax views of a portion of a physical environment. The processor may determine a first average pixel value of pixels of the first block. The processor may identify a second block of pixels in the region. The processor may determine a second average pixel value of pixels of the second block. The processor may generate an updated pixel value for a pixel location of the first block or the second block. The updated pixel value may be based at least in part on the first average pixel value and the second average pixel value.

Abstract: Various examples are directed to methods and systems for compressing a binary mask. A computer device may determine that a frequency of a less frequent pixel value state of the binary mask is less than a threshold frequency and may select a block size corresponding to the threshold frequency. The computing device may determine a first run length number of pixel value blocks of the block size that are positioned consecutively in the binary mask sequence and that comprise a first pattern of pixel values and write the first run length number to a first compression sequence.

Abstract: A system uses machine learning techniques to train models for determining whether to activate high dynamic range (HDR) imaging in devices. The system may train machine learning components/models based on customized image metrics (such as customized histograms) that can be used across devices. The system may also train HDR systems on non-image data like GPS data, etc. to make improved HDR recommendations based on the context of a particular image.

Abstract: Devices, systems and methods are disclosed that detect and interpret the meaning of visual indicators (such as LEDs) of devices, and provide information and solutions to potential malfunctions to users. For example, image data of the visual indicators can be captured by an image capture device (such as a camera). The image data may then be analyzed to identify an object captured in the image data. Luminescence of pixel values in the image data corresponding to a visual indicator of the object may be used to determine a sequence of the visual indicator. Then, information corresponding to the meaning of the sequence may be sent to a device of a user or owner of the object.

Abstract: An ambient light sensor may be used to measure ambient light in an environment that surrounds a device. The measured ambient light may be used to adjust display settings employed by the device, and thereby improve a user experience during interaction with the device in the environment. During use of the device that includes an ambient light sensor, the ambient light sensor may become obscured (blocked, covered, etc.) by a user's finger, a cover, dirt, and/or by another object that obstructs the ambient light sensor's ability to detect light from the environment. Various techniques may be used to determine obstruction of the ambient light sensor. In some embodiments, changes in display settings may be compared to measured changes in ambient light to determine whether a light sensor is obstructed. In some embodiments, use of a second light sensor may validate whether a first light sensor is obstructed.