A miniaturized active dual pixel stereo system and method for close range depth extraction includes a projector adapted to project a locally distinct projected pattern onto an image of a scene and a dual pixel sensor including a dual pixel sensor array that generates respective displaced images of the scene. A three-dimensional image is generated from the displaced images of the scene by projecting the locally distinct projected pattern onto the image of the scene, capturing the respective displaced images of the scene using the dual pixel sensor, generating disparity images from the respective displaced images of the scene, determining depth to each pixel of the disparity images, and generating the three-dimensional image from the determined depth to each pixel. A three-dimensional image of a user's hands generated by the active dual pixel stereo system may be processed by gesture recognition software to provide an input to an electronic eyewear device.

A vehicle occupant monitoring system, OMS, comprises: an image acquisition device comprising an image sensor and a lens assembly having a varying transmissivity across a field of view of the image sensor; at least one infra-red, IR, light source disposed within a cabin of the vehicle and being configured to illuminate at least one occupant of the vehicle with varying illumination across the field of view of the image sensor; and an image processing pipeline configured to obtain and pre-process an image acquired from the image sensor in accordance with a lens shading map and a cabin illumination map in order to compensate for both the varying transmissivity and the varying illumination in order to provide a more uniformly illuminated image to a controller for further analysis.

A vehicle occupant monitoring system, OMS, comprises an image acquisition device with a rolling shutter image sensor configured to selectively operate in either: a full-resolution image mode where an image frame corresponding to the full image sensor is provided; or a region of interest, ROI, mode, where an image frame corresponding to a limited portion of the image sensor is provided. An object detector is configured to receive a full-resolution image from the image sensor and to identify a ROI corresponding to an object of interest within the image. A controller is configured to obtain an image corresponding to the ROI from the image sensor operating in ROI mode, the image having an exposure time long enough for all rows of the ROI to be illuminated by a common pulse of light from at least one infra-red light source and short enough to limit motion blur within the image.

A touch sensing apparatus, touch devices including the touch sensing apparatus and electronic appliances including the touch sensing apparatus are provided. The touch sensing apparatus may include a substrate including an effective area and a non-effective area, a cavity provided in the non-effective area, a fingerprint sensor provided on a bottom surface of the cavity, and a first decorative layer provided inside the cavity and having a plurality of oxide layers. The first decorative layer may include at least one metal layer provided between the plurality of oxide layers. The metal layer may have a thickness ranging from 10 nm to 50 nm.

A volume holographic film (such as a photopolymer) that is pre-recorded with patterns subsequently is used to encode LED or low-power laser light reflections from an eye into a binary pattern that can be read at very high speeds by a relatively simple complementary metal-oxide-semiconductor (CMOS) sensor that may be similar to a high framerate, low resolution mouse sensor. The low-resolution mono images from the film are translated into eye poses using, for instance, a look up table that correlates binary patterns to X, Y positions or using a pre-trained convolutional neural network to robustly interpret many variations of the binary patterns for conversion to X, Y positions.

A volume holographic film (such as a photopolymer) that is pre-recorded with patterns subsequently is used to encode LED or low-power laser light reflections from an eye into a binary pattern that can be read at very high speeds by a relatively simple complementary metal-oxide-semiconductor (CMOS) sensor that may be similar to a high framerate, low resolution mouse sensor. The low-resolution mono images from the film are translated into eye poses using, for instance, a look up table that correlates binary patterns to X, Y positions or using a pre-trained convolutional neural network to robustly interpret many variations of the binary patterns for conversion to X, Y positions.

A wiring board for a fingerprint sensor includes an insulating board including insulating layers; outer strip electrodes disposed on the insulating layer in an uppermost layer, and side by side in a first direction; inner strip electrodes disposed on the insulating layer in a next layer contacting the insulating layer in the uppermost layer, and side by side in a second direction orthogonal to the first direction; a pad electrode disposed on the insulating layer in the uppermost layer, and on the inner strip electrodes and between the outer strip electrodes; and a via conductor extending through the insulating layer in an outermost layer between the pad electrode and the inner strip electrodes and electrically connecting the pad electrode and the inner strip electrodes to each other. The via conductor has an elliptical shape that is long in the first direction in top view.

Systems and methods for performing direct conversion of image sensor data to image analytics are provided. One such system for directly processing sensor image data includes a sensor configured to capture an image and generate corresponding image data in a raw Bayer format, and a convolution neural network (CNN) coupled to the sensor and configured to generate image analytics directly from the image data in the raw Bayer format. Systems and methods for training the CNN are provided, and may include a generative model that is configured to convert RGB images into estimated images in the raw Bayer format.

Systems and methods for performing direct conversion of image sensor data to image analytics are provided. One such system for directly processing sensor image data includes a sensor configured to capture an image and generate corresponding image data in a raw Bayer format, and a convolution neural network (CNN) coupled to the sensor and configured to generate image analytics directly from the image data in the raw Bayer format. Systems and methods for training the CNN are provided, and may include a generative model that is configured to convert RGB images into estimated images in the raw Bayer format.

A computer accesses a plurality of image frames. The computer identifies, within the plurality of image frames, a plurality of vehicle front vehicle back detections. The computer pairs at least a subset of the plurality of vehicle back detections with vehicle front detections. A given vehicle back detection is paired with a given vehicle front detection based on camera angle relative to a predefined axis. The computer assigns, using each of a plurality of pools, a score to each vehicle front detection—vehicle back detection pair, each non-paired vehicle front detection, and each non-paired vehicle back detection. Each pool comprises a data structure representing a scoring mechanism and a set of detections. The computer assigns each detection to a pool that assigned a highest score to that detection. Upon determining that a given pool comprises at least n detections: the computer labels the given pool as representing a specific vehicle.