A computing system receives, from a client device, image data describing an image captured by an optical sensor of the client device. The computing system compares the image to a set of reference images that include associated metadata describing a real-world feature depicted by the respective reference image. The computing system determines, based on the comparison, a subset of reference images that are similar to the image, and then determines, based on associated metadata of the subset of reference images, that the image captured by the optical sensor of the client device depicts a first real-world feature. The computing system selects a subset of media overlays related to the first real-world feature based on metadata associated with each media overlay that describes the respective media overlay. The computing system transmits the subset of media overlays to the client device.

An image quality assessment solution analyzes an image quality and a correlation of an image to an item description associated with the item. The content quality assessment may assign a quality score to the image based on a composition of the image and/or correlation with the image description. The score may be based on a model that is trained to analyze images using a learning model. Based on the image score, a correlation score, or other scores, the user may be given feedback on how to improve an image. A service provider providing this service may use the score to influence recommendation results that use the images.

Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

A managed notification system compares image(s) and/or indicia relating to the image(s) and where there is a match selectively provides a notification of the same.

A system that includes a first edge node and a second edge node in signal communication with one or more interior nodes. The first edge node is configured to receive a correlithm object from a first device outside of the network, to identify an input correlithm object from the first node table with the shortest distance, to fetch a corresponding correlithm object from the first node table, and to send the correlithm object to the one or more interior nodes. The second edge node is configured to receive a correlithm object from the one or more interior nodes, to identify an input correlithm object from a second node table with the shortest distance, to fetch a corresponding correlithm object from the second node table, and to send the correlithm object to a second device outside of the network.

A device that includes a converter engine configured to receive an input signal at one of a first input and a second input. In response to receiving the input signal at the first input, the device is configured to identify a real world value in a converter table based on the input signal, fetch a correlithm object linked with the real world value, and to output the identified correlithm object as the first output signal. In response to receiving the input signal at the second input, the device is configured to identify a correlithm object from the converter table with the shortest distance, to fetch a real world value from the converter table linked with the identified correlithm object, and to output the identified real world value as the second output signal.

A system is provided for correcting an error of a camera for a camera monitoring system (CMS), including a camera attached to a side of a vehicle, a memory configured to store processor-executable instructions for correcting an error of an image captured by the camera, and a processor configured with the processor-executable instructions to detect a correction point is the image captured by the camera, generate a correction parameter based on a resultant value of a comparison between the detected correction point and a preset correction point, and correct the error of the image based on the generated correction parameter.

Apparatuses, systems and methods are provided for generating a vehicle driver model. More particularly, apparatuses, systems and methods are provided for generating a vehicle driver model for a particular vehicle based on vehicle interior image data.

The various embodiments described herein include methods, devices, and systems for coupling wireless devices. In one aspect, a method includes receiving, at a server, an image captured by a first electronic device; obtaining, based at least in part on the received image, connection information for facilitating a wireless connection between the first electronic device and a second electronic device; and transmitting to the first electronic device the obtained connection information for facilitating a wireless connection between the first electronic device and a second electronic device.

Provided are a method and a device for offline identity authentication. The method includes: acquiring two or more images for identity authentication to constitute a to-be-authenticated multivariate image group; extracting a concatenated PCA convolution feature of each of the images in the to-be-authenticated multivariate image group, to obtain feature vectors; fusing information on the images in the to-be-authenticated multivariate image group based on the feature vectors according to a score fusion strategy with a supervisory signal, to obtain a fusion vector; and inputting the fusion vector to a pre-trained SVM classifier to authenticate and determine whether the images in the to-be-authenticated multivariate image group are consistent with one another, to obtain an identity authentication result.