Abstract: This disclosure provides methods, devices, and systems for low-light imaging. In some implementations, an image processor may be configured to reduce or remove noise associated with an image based, at least in part, on a neural network. For example, the neural network may be trained to infer a denoised representation of the image. In some aspects, the image processor may scale the brightness level of the image to fall within a normalized range of values associated with the neural network. In some other aspects, a machine learning system may scale the brightness levels of input images to match the brightness levels of ground truth images used to train the neural network. Still further, in some aspects, the machine learning system may scale the brightness levels of the input images and the brightness levels of the ground truth images to fall within the normalized range of values during training.

Abstract: This disclosure provides methods, devices, and systems for neural network inferencing. The present implementations more specifically relate to performing inferencing operations on high dynamic range (HDR) image data in a lossless manner. In some aspects, a machine learning system may receive a number (K) of bits of pixel data associated with an input image and subdivide the K bits into a number (M) of partitions based on a number (N) of bits in each operand operated on by an artificial intelligence (AI) accelerator, where N<K. For example, the K bits may represent a pixel value associated with the input image. In some implementations, the AI accelerator may perform an inferencing operation based on a neural network by processing the M partitions, in parallel, as data associated with M channels, respectively, of the input image.

Abstract: This disclosure provides methods, devices, and systems for low-light imaging. The present implementations more specifically relate to selecting images that can be used for training a neural network to infer denoised representations of images captured in low light conditions. In some aspects, a machine learning system may obtain a series of images of a given scene, where each of the images is associated with a different SNR (representing a unique combination of exposure and gain settings). The machine learning system may identify a number of saturated pixels in each image and classify each of the images as a saturated image or a non-saturated image based on the number of saturated pixels. The machine learning system may then select the non-saturated image with the highest SNR as the ground truth image, and the non-saturated images with lower SNRs as the input images, to be used for training the neural network.

Abstract: This disclosure provides methods, devices, and systems for training machine learning models. The present implementations more specifically relate to techniques for automating the annotation of data for training machine learning models. In some aspects, a machine learning system may receive a reference image depicting an object of interest with one or more annotations and also may receive one or more input images depicting the object of interest at various distances, angles, or locations but without annotations. The machine learning system maps a set of points in the reference image to a respective set of points in each input image so that the annotations from the reference image are projected onto the object of interest in each input image. The machine learning system may further train a machine learning model to produce inferences about the object of interest based on the annotated input images.

Abstract: This disclosure provides methods, devices, and systems for neural network inferencing. The present implementations more specifically relate to performing inferencing operations on high dynamic range (HDR) image data in a lossless manner. In some aspects, a machine learning system may receive a number (K) of bits of pixel data associated with an input image and subdivide the K bits into a number (M) of partitions based on a number (N) of bits in each operand operated on by an artificial intelligence (AI) accelerator, where N<K. For example, the K bits may represent a pixel value associated with the input image. In some implementations, the AI accelerator may perform an inferencing operation based on a neural network by processing the M partitions, in parallel, as data associated with M channels, respectively, of the input image.

Abstract: This disclosure provides methods, devices, and systems for low-light imaging. The present implementations more specifically relate to selecting images that can be used for training a neural network to infer denoised representations of images captured in low light conditions. In some aspects, a machine learning system may obtain a series of images of a given scene, where each of the images is associated with a different SNR (representing a unique combination of exposure and gain settings). The machine learning system may identify a number of saturated pixels in each image and classify each of the images as a saturated image or a non-saturated image based on the number of saturated pixels. The machine learning system may then select the non-saturated image with the highest SNR as the ground truth image, and the non-saturated images with lower SNRs as the input images, to be used for training the neural network.

Abstract: This disclosure provides methods, devices, and systems for low-light imaging. In some implementations, an image processor may be configured to reduce or remove noise associated with an image based, at least in part, on a neural network. For example, the neural network may be trained to infer a denoised representation of the image. In some aspects, the image processor may scale the brightness level of the image to fall within a normalized range of values associated with the neural network. In some other aspects, a machine learning system may scale the brightness levels of input images to match the brightness levels of ground truth images used to train the neural network. Still further, in some aspects, the machine learning system may scale the brightness levels of the input images and the brightness levels of the ground truth images to fall within the normalized range of values during training.

Abstract: A method and apparatus for event-based media playback. A media device infers one or more actionable events in a media content item using one or more neural network models and determines a respective start location for each of the actionable events in the media content item. The media device receives user input indicating a selection of one of the actionable events and selectively initiates playback of the media content item at the start location associated with the selected actionable event.

Abstract: A method and apparatus for event-based media playback. A media device infers one or more actionable events in a media content item using one or more neural network models and determines a respective start location for each of the actionable events in the media content item. The media device receives user input indicating a selection of one of the actionable events and selectively initiates playback of the media content item at the start location associated with the selected actionable event.

Abstract: Systems and methods for updating an enrollment template having a plurality of enrollment views of a biometric input object. A determination is made as to whether a new input biometric view is a candidate view for template update based on a match criterion, and a determination is made as to whether the new input biometric view increases coverage of the biometric input object by the enrollment template. The new input biometric view is added to the enrollment template as a new enrollment view in response to determining that the new biometric view i) is a candidate view for template update, and ii) increases coverage of the biometric input object by the enrollment template.