Abstract: This disclosure provides methods, devices, and systems for object detection. The present implementations more specifically relate to techniques for improving distant object detection in memory-constrained computer vision systems. In some aspects, a computer vision system may include an ROI extraction component, a feature pyramid network (FPN) having a number (N) of pyramid levels, and N network heads associated with the N pyramid levels, respectively. The FPN extracts N feature maps from an input image, where the N feature maps are associated with the N pyramid levels, respectively, and each of the N network heads performs an object detection operation on a respective feature map of the N feature maps. The ROI extraction component selects a region of the feature map associated with the lowest pyramid level for distant object detection so that the object detection operation performed on that feature map is confined to the selected region.

Abstract: This disclosure provides methods, devices, and systems for machine learning. The present implementations more specifically relate to automatons that can acquire input images and ground truth images for training neural network models. In some aspects, a system for acquiring training data may include a camera, an electronic display, and an apparatus configured to maintain the camera in a stationary position while moving the electronic display in and out of the camera's field-of-view (FOV). In some aspects, the system may further include a controller configured to acquire training data via the camera based on the positioning of the electronic display. In some implementations, the controller may acquire ground truth images of a scene while the electronic display is covering the camera's FOV. In some other implementations, the controller may acquire input images of the scene while the electronic display is outside the camera's FOV.

Abstract: A memory device having a plurality of groups of words is provided. Each group of the plurality of groups of words are stored in a contiguous section of a memory and each group of the plurality of groups of words includes a plurality of words. The plurality of words each have a plurality of elements with a same set of index values for a first dimension of a tensor and a same set of index vales for a second dimension of a tensor. The plurality of words have a different index value for a third dimension of the tensor.

Abstract: This disclosure provides methods, devices, and systems for object detection. The present implementations more specifically relate to region of interest (ROI) inferencing techniques that can be implemented using a single object detection model. In some aspects, a computer vision system maps a set of grid cells to an input image so that each grid cell includes a respective portion of the image, and where each of the grid cells is assigned a respective priority value. The system selects an ROI of the image based on the priority value assigned to each grid cell and performs, on the ROI, an inferencing operation associated with an object detection model. The system updates the priority values for one or more of the grid cells based on a result of the inferencing operation. The system then selects another ROI based on the updated priority values and perform the inferencing operation on the new ROI.

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: An input device is provided. The input device comprises a printed circuit board (PCB) assembly comprising: touch sensors configured to detect a user input from a user; a haptic coil configured to provide haptic feedback to the user in response to detecting the user input; and a near-field communication (NFC) antenna configured to facilitate NFC communications, wherein the haptic coil and the NFC antenna are on a same PCB of the PCB assembly.

Abstract: A method is provided. The method comprises obtaining, by a processing system and using a piezoelectric device, piezoelectric signals associated with user input on a sensing region of an input device; obtaining, by the processing system and using a corrective device, corrective signals associated with the user input on the sensing region of the input device; determining, by the processing system and based on the piezoelectric signals and the corrective signals, one or more events to be performed in response to the user input; and performing, by the processing system, the one or more events.

Abstract: A memory device includes a first word and a second word. The first word has a first subset of a plurality of elements. The first subset of the plurality of elements each have a first set of sequential index values along a first dimension of a tensor, a first single index value for a second dimension of the tensor, and a second single index value for a third dimension of the tensor. The second word has a second subset of the plurality of elements. The second subset of the plurality of elements each have the first set of sequential index values along the first dimension of the tensor that is the same as the first word, the first single index value for the second dimension of the tensor that is the same as the first word, and a third single index value for the third dimension of the tensor that is different than the second single index value for the first word. The second word is adjacent to the first word in memory.

Abstract: An input device is provided. The input device comprises a printed circuit board (PCB) assembly comprising: touch sensors configured to detect a user input from a user; a haptic coil configured to provide haptic feedback to the user in response to detecting the user input; and a near-field communication (NFC) antenna configured to facilitate NFC communications, wherein the haptic coil and the NFC antenna are on a same PCB of the PCB assembly.

Abstract: A method is provided. The method comprises obtaining, by a processing system and using a piezoelectric device, piezoelectric signals associated with user input on a sensing region of an input device; obtaining, by the processing system and using a corrective device, corrective signals associated with the user input on the sensing region of the input device; determining, by the processing system and based on the piezoelectric signals and the corrective signals, one or more events to be performed in response to the user input; and performing, by the processing system, the one or more events.

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 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: A method is provided. The method comprises obtaining, by a processing system and using a piezoelectric device, piezoelectric signals associated with user input on a sensing region of an input device; obtaining, by the processing system and using a corrective device, corrective signals associated with the user input on the sensing region of the input device; determining, by the processing system and based on the piezoelectric signals and the corrective signals, one or more events to be performed in response to the user input; and performing, by the processing system, the one or more events.

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: A memory device includes a first word and a second word. The first word has a first subset of a plurality of elements. The first subset of the plurality of elements each have a first set of sequential index values along a first dimension of a tensor, a first single index value for a second dimension of the tensor, and a second single index value for a third dimension of the tensor. The second word has a second subset of the plurality of elements. The second subset of the plurality of elements each have the first set of sequential index values along the first dimension of the tensor that is the same as the first word, the first single index value for the second dimension of the tensor that is the same as the first word, and a third single index value for the third dimension of the tensor that is different than the second single index value for the first word. The second word is adjacent to the first word in memory.

Abstract: An input device is provided. The input device comprises a printed circuit board (PCB) assembly comprising: touch sensors configured to detect a user input from a user; a haptic coil configured to provide haptic feedback to the user in response to detecting the user input; and a near-field communication (NFC) antenna configured to facilitate NFC communications, wherein the haptic coil and the NFC antenna are on a same PCB of the PCB assembly.

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: Fingerprint sensors include a plurality of optical sensor elements, a collimator filter disposed above the plurality of optical sensor elements, and a display disposed above the collimator filter, wherein the display is a fingerprint imaging light source. The collimator filter has a plurality of apertures, and the plurality of optical sensor elements is configured to receive light transmitted through one aperture of the plurality of apertures.

Abstract: A method for determining an amount of ambient light illumination is disclosed. The method includes: estimating a contribution per color component in an optical sensing region of an input frame to be displayed on a display device, wherein the optical sensing region corresponds to a location of an optical sensor in the display device, wherein the input frame comprises digital information for each color component for each pixel of the input frame; causing the optical sensor to detect an amount of illumination in the optical sensing region based on illuminating the display device with a representation of the input frame; and determining the amount of ambient light illumination based on the estimated contribution per color component in the optical sensing region of the input frame and the amount of illumination in the optical sensing region detected by the optical sensor.