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.

An imaging device in which an imaging sensor that captures an image and a display unit that displays the image are provided includes: an image processing unit configured to perform roll correction on the image; a control unit configured to control an output of the image; and a display control unit configured to display the output image on the display unit. The image processing unit performs the roll correction such that a degree of correction of the roll correction when the image is to be output to only the display unit is less than a degree of correction of the roll correction when the image is to be output to the display unit and an output destination other than the display unit.

An apparatus including a location device and a processor. The location device may be configured to determine location coordinates of the apparatus. The processor may be configured to receive video frames captured by a capture device, perform video analysis on the video frames to detect objects in the video frames and extract metadata corresponding to the objects detected in the video frames, correlate the metadata with the location coordinates, determine a distance from the apparatus to the objects in the video frames and calculate an absolute location of the objects in response to the distance and the location coordinates. The distance may be determined by comparing a size of the objects detected in the video frames with a known size of the objects. The absolute location for the objects in the video frames may be added to the metadata.

A non-transitory computer-readable recording medium stores a determination program for causing a computer to execute processing including: specifying a difference between feature amounts of a plurality of first images that is captured in chronological order or of which the difference between the feature amounts is equal to or less than a threshold; referring to information in which the difference is associated with a data augmentation processing type and determining one or a plurality of data augmentation processing types used for processing of generating machine learning data on the basis of the specified difference between the feature amounts; and outputting a result of the determination processing.

Systems and methods for training an adapter network that adapts a model pre-trained on synthetic images to real-world data are disclosed herein. A system may include a processor and a memory in communication with the processor and having machine-readable that cause the processor to output, using a neural network, a predicted scene that includes a three-dimensional bounding box having pose information of an object, generate a rendered map of the object that includes a rendered shape of the object and a rendered surface normal of the object, and train the adapter network, which adapts the predicted scene to adjust for a deformation of the input image by comparing the rendered map to the output map acting as a ground truth.

Ac device includes an acquisition unit configured to acquire first information regarding shake, a calculation unit configured to input the first information to a machine learning model and output second information regarding a type of the shake, and a first control unit configured to control an image stabilization using the second information. By using the second information output from the calculation unit based on the first information before a capturing instruction is given, the first control unit controls an image stabilization after the capturing instruction is given.

The present disclosure discloses an apparatus and a method for detecting a facial pose, an image processing system, and a storage medium. The apparatus comprises: an obtaining unit to obtain at least three keypoints of at least one face from an input image based on a pre-generated neural network, wherein coordinates of the keypoints obtained via a layer in the neural network for obtaining coordinates are three-dimensional coordinates; and a determining unit to determine, for the at least one face, a pose of the face based on the obtained keypoints, wherein the determined facial pose includes at least an angle. According to the present disclosure, the accuracy of the three-dimensional coordinates of the facial keypoints can be improved, thus the detection precision of a facial pose can be improved.

Embodiments of the present disclosure provide a method and device for recognizing a state of an electrical device. The method includes obtaining an image of the electrical device in a field, wherein the electrical device is disposed on a plane of a predefined polygon in the field; obtaining an original appearance image of the electrical device in the field by recovering the predefined polygon in the image to an original appearance of the predefined polygon; and determining the state of the electrical device based on the original appearance image of the electrical device in the field.

The invention relates to a method of reconstructing a fingerprint image from a plurality of fingerprint subimages captured by an optical microlens array fingerprint sensor, and a sensor system performing the method. In an aspect, a method of reconstructing a fingerprint image from a plurality of fingerprint subimages captured by an optical microlens array fingerprint sensor arranged under a touch-display of a device is provided. The method comprises capturing an image of a fingerprint with the fingerprint sensor, extracting, from the captured fingerprint image, a fingerprint subimage for each microlens in the array, normalizing the corrected fingerprint subimages with corresponding subimages of a uniformity calibration image with known uniformity properties, rotating the normalized fingerprint subimages 180 degrees while maintaining their relative position with respect to the captured fingerprint image from which the fingerprint subimages are extracted, and combining the rotated and normalized fingerprint subimages into a fingerprint image.

In one aspect, a retail store includes a multitude of cameras, including a plurality of 3D cameras, and a plurality of other cameras. Certain of the cameras provide imagery from which a shopper's track through the store is monitored, and certain of the cameras are positioned to detect removal of items from store shelves. The store also includes a computer system that provides a database of information about store layout, indicating stock locations of different items. The computer system receives imagery from the cameras (or information derived from such imagery) and uses this data, together with information from the database and information derived from other sensors in the store, to produce a probabilistic tally of items selected by a store shopper. This tally includes an item bearing a barcode, but is produced without reading the barcode. Each item on the tally is associated with a confidence score that meets a computer system-determined threshold. A great number of other features and arrangements are also detailed.