In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.

An illustrative image descriptor generation system generates a descriptor listing that includes a plurality of image descriptors corresponding to different feature points included within an image. Based on the descriptor listing, the system generates a geometric map representing the plurality of image descriptors in accordance with respective geometric positions of the corresponding feature points of the image descriptors within the image. Based on the geometric map, the system determines a proximity listing for a primary image descriptor within the plurality of image descriptors. The proximity listing indicates a subset of image descriptors that are geometrically proximate to the primary image descriptor within the image. Based on the proximity listing, the system selects a secondary image descriptor from the subset of image descriptors and combines the primary and secondary image descriptors to form a composite image descriptor. Corresponding methods and systems are also disclosed.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image representation neural network.

A learning apparatus executes processing of: a first neural network that extracts a first feature of a target in image data; a second neural network that extracts a second feature of the target in the image data using a network structure different from the first neural network; and a learning support neural network that extracts a third feature from the first feature extracted by the first neural network. Here, the second feature and the third feature are biased features for the target. The learning apparatus trains the learning support neural network so that the second feature and the third feature come closer, and trains the first neural network so that the third feature appearing in the first feature extracted by the first neural network is reduced.

Methods, systems, and computer program products are provided for signature verification. Signature verification may be provided for target signatures using genuine signatures. A signature verification model pipeline may extract features from a target signature and a genuine signature, encode and submit both to a neural network to generate a similarity score, which may be repeated for each genuine signature. A target signature may be classified as genuine, for example, when one or more similarity scores exceed a genuine threshold. A signature verification model may be updated or calibrated at any time with new genuine signatures. A signature verification model may be implemented with multiple trainable neural networks (e.g., for feature extraction, transformation, encoding, and/or classification).

Provided herein are systems and methods for applying adaptive classes thresholds to enhance object detection Machine Learning (ML) models by receiving a plurality of labeled feature vectors extracted from a plurality of images associated with a plurality of objects, one or more subsets of the plurality of feature vectors are associated with respective object(s) and labeled accordingly, computing an adaptive threshold for each object in a plurality of iterations, each iteration comprising: (1) computing deviation of a respective feature vector of the subset from an aggregated feature vector, (2) computing, in case the deviation is within a predefined value, a threshold enclosing the respective feature vector, and (3) adjusting the adaptive threshold to enclose the threshold of the respective feature vector and outputting the adaptive threshold(s) for classifying unlabeled feature vectors to class(s) of respective object(s) associated with the adaptive threshold(s) in which the unlabeled feature vectors fall.

Embodiments of the invention provide systems and methods of generating a complete and accurate geometrically optimized environment. Stereo pair images depicting an environment are selected from a plurality of images to generate a Digital Surface Model (DSM). Characteristics of objects in the environment are determined and identified. The geometry of the objects may be determined and fit with polygons and textured facades. By determining the objects, the geometry, and the material from original satellite imagery and from a DSM created from the matching stereo pair point clouds, a complete and accurate geometrically optimized environment is created.

Embodiments of the present disclosure describe mechanisms for a radar-based indoor localization and tracking system. One example can include monitoring unit that includes a radar source, a camera unit, and one or more processors coupled to the radar element and the camera unit. The monitoring unit is configured to generate point cloud data associated with an object; execute Point Cloud Library (PCL) preprocessing based, at least, on the point cloud data; execute Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering; execute multi-object tracking on the object; and execute an image PCL overlay based on the point cloud data to generate real-time data associated with the object.

A method for measuring a distance between trace lines of a 3D braided material, including: (S1) establishing a vision data acquisition system using a vision sensor; (S2) acquiring, by the vision data acquisition system, a training data of the trace lines of the 3D braided material; (S3) constructing a deep learning model for recognizing the trace lines of the 3D braided material; and inputting the training data acquired in step (S2) to the deep learning model to obtain a trained deep learning model; and (S4) positioning a location of the trace lines of the 3D braided material in batch images according to the trained deep learning model obtained in step (S3); and measuring a distance between adjacent trace lines.

A method for recognizing a text in an image includes: obtaining a plurality of recognition results of a to-be-recognized text in an image according to a plurality of recognition methods (S201); obtaining semantic information of the recognition results (S202); obtaining feature information of the image, where the feature information of the image can represent information around the to-be-recognized text in the image (S203); and determining a target recognition result of the to-be-recognized text from the plurality of recognition results based on the feature information of the image and the semantic information of the plurality of recognition results (S204). According to the method, accuracy of determining the most accurate recognition result from the plurality of recognition results can be improved, that is, a precise recognition result can be obtained.