A sensor calibration system configured to receive a first frame of one or more markers on a repositionable platform at a first location within a space from a sensor. The system is further configured to determine pixel locations in the first frame for a first marker and a second marker from among the one or more markers. The system is further configured to receive distance information that corresponds with a distance between the platform and distance measuring devices. The system is further configured to determine (x,y) coordinates for the first marker and the second marker based on the distance information. The system is further configured to generate a homography based on the (x,y) coordinates and pixel locations of the first marker and the second marker. The homography includes coefficients that translate between pixel locations in the first frame of the sensor and (x,y) coordinates in the global plane.

Provided herein are a calibration method for a fingerprint sensor and a display device using the calibration method, where, in the calibration method for a fingerprint sensor, the fingerprint sensor includes a substrate, a light-blocking layer located on a first surface of the substrate and having openings formed in a light-blocking mask, a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements, and a sensor layer located on a second surface of the substrate and having a plurality of photosensors; and the calibration method includes generating calibration data through white calibration and dark calibration, and applying offsets to the plurality of photosensors using the calibration data.

A method includes exciting, at a first time period, a first set of pixels in an excitation array, wherein the first set of pixels comprises more than one pixel, and no pixel in the first set of pixels is adjacent to another pixel in the first set of pixels. The method also includes exciting, at a second time period, a second set of pixels in the excitation array wherein the second set of pixels comprises more than one pixel, and no pixel in the second set of pixels is adjacent to another pixel in the second set of pixels. The method retrieves excitation data, wherein the excitation data is comprised of data from the first set of pixels and data from the second set of pixels, and the excitation data is capable of being combined to reconstruct an image of a target object for rendering on a display.

A fingerprint sensing module comprising a fingerprint sensor device having a sensing array arranged on a first side of the device, the sensing array comprising an array of fingerprint sensing elements. The fingerprint sensor device comprises connection pads for connecting to external circuitry. The fingerprint sensing module further comprises a fingerprint sensor device cover structure, arranged to cover the fingerprint sensor device, having a first side configured to be touched by a finger, thereby forming a sensing surface of the sensing module, and a second side facing the sensing array, wherein the cover structure comprises conductive traces for electrically connecting the fingerprint sensor module to external circuitry, and wherein a surface area of the cover structure is larger than a surface area of the sensor device. The fingerprint sensor device comprises wire-bonds electrically connecting the connection pads of the fingerprint sensing device to the conductive traces of the cover structure.

An in-situ gemstone characteristic analysis system, apparatus and method. Included are an in-situ rig comprising a plurality of movable camera sensors, a plurality of stimulus dispensers, and a plurality of environmental condition dispensers; a first input remote from the in-situ rig for receiving output from the camera sensors responsive to applications to a gemstone in the in-situ rig of at least the stimulus and the environmental conditions; a comparator communicative with the first input and having accessible thereto a plurality of gemstone characteristics resultant from substantially similar ones of the stimulus and the environmental conditions, and capable of comparing the output from the camera sensors to the plurality of gemstone characteristics and finding a match, under control from at least one computing processor applying a plurality of machine learning rules; and a match output in a case of the match being within a predetermined threshold.

An image sensor is positioned such that a field-of-view of the image sensor encompasses at least a portion of a rack storing items. The image sensor generates angled-view images of the items stored on the rack. A tracking subsystem determines that a person is within a threshold distance of the rack and receives image frames of the angled-view images. A pixel position of a wrist of the person is determined in at least a subset of the received image frames, thereby determining a set of pixel positions of the wrist. An aggregated wrist position is determined based on the set of pixel positions. If the aggregated wrist position is determined to correspond to a position on a shelf of the rack, a trigger signal is provided indicating a shelf-interaction event has occurred.

A sensor calibration system configured to receive a first frame of one or more markers on a repositionable platform at a first location within a space from a sensor. The system is further configured to determine pixel locations in the first frame for a first marker and a second marker from among the one or more markers. The system is further configured to receive distance information that corresponds with a distance between the platform and distance measuring devices. The system is further configured to determine (x,y) coordinates for the first marker and the second marker based on the distance information. The system is further configured to generate a homography based on the (x,y) coordinates and pixel locations of the first marker and the second marker. The homography includes coefficients that translate between pixel locations in the first frame of the sensor and (x,y) coordinates in the global plane.

Computerized systems, methods, apparatuses, and computer-readable storage media are provided for generating a 3D map of an environment and/or for utilizing the 3D map to enable a user to control smart devices in the environment and/or to interact with a person in the environment. To generate the 3D map, perform the control, and/or interact with the person, a plurality of neuromuscular sensors may be worn by the user. The sensors may be arranged on a carrier worn by the user, and may be configured to sense neuromuscular signals from the user. A camera configured to capture information about the environment may be arranged on the carrier worn by the user. The sensors and the camera provide data to a computer processor coupled to a memory.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.

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.