A processor-implemented method with data recognition includes: extracting input feature data from input data; calculating a matching score between the extracted input feature data and enrolled feature data of an enrolled user, based on the extracted input feature data, common component data of a plurality of enrolled feature data corresponding to the enrolled user, and distribution component data of the plurality of enrolled feature data corresponding to the enrolled user; and recognizing the input data based on the matching score.

A computer-implemented method for processing an electronic image may include receiving, by an artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital whole slide images (WSIs) and extracting one or more vectors of features from one or more foreground tiles of tile images of the one or more digital WSIs. The method may include running a trained machine learning model on the one or more vectors of features and determining, based on an output of the trained machine learning model, whether one or more quality issues are present in the one or more digital WSIs.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

A computational lithography process uses machine learning models. An aerial image produced by a lithographic mask is first calculated using a two-dimensional model of the lithographic mask. This first aerial image is applied to a first machine learning model, which infers a second aerial image. The first machine learning model was trained using a training set that includes aerial images calculated using a more accurate three-dimensional model of lithographic masks. The two-dimensional model is faster to compute than the three-dimensional model but it is less accurate. The first machine learning model mitigates this inaccuracy.

A method for clustering phishing web resources based on visual content image, executed on a computer device comprising at least a processor and memory, and the method comprises the following steps: receiving references to a set of phishing web resources; retrieving at least one image of the visual content of each web resource of the set; processing the content of each visual content image associated with one of the set web resources, while contouring the elements on each image of the phishing web resource visual content; filtering the identified contours in each visual content image by removing the identical contours; combining the web resource associated with the compared contours and the cluster based on pairwise comparison of the identified contours and cluster contours, wherein, if the similarity value overrides the threshold value, otherwise, creating a new cluster for the web resource; storing references to web resources associated with corresponding contours of the content from a set of specified clusters in a database.

Devices, methods, and non-transitory program storage devices (NPSDs) are disclosed herein to allow individual users an opportunity to create customized instances of three-dimensional (3D) avatars, wherein each instance of the avatar may be customized to have particular visual characteristics and/or accessories that may reflect an individual user's appearance. When images are captured, e.g., during an individual user's avatar enrollment or customization process, novel shape matching techniques may be employed between two-dimensional (2D) objects of interest (e.g., eyeglasses frames) identified in the enrollment image and 3D models stored in one or more 3D model object libraries. A ranked listing of 3D models from an object library that provide the best shape matches to the identified 2D objects of interest in the captured image may automatically be determined and/or presented to the user for selection, e.g., via a user interface, for use in the creation and/or customization of the user's 3D avatar.

A method of determining the steering angle of a landing gear assembly of an aircraft is disclosed including scanning the landing gear assembly with a lidar system to generate a set of three-dimensional position data points, each position data point including a set of three orthogonal position values. A two-dimensional image from the set of three-dimensional position data points, by converting a position value of each of the three-dimensional position data points to an image property value of a set of image property values. A boundary of an area of the two-dimensional image of which each position data point has the same image property value is identified, where the area corresponds to a component of the landing gear assembly. The steering angle of the landing gear assembly is then determined from the shape and/or orientation of the identified boundary.

A filling rate measurement method includes: obtaining a space three-dimensional model generated by measuring a first storage having an opening and a first storage space in which a measurement target is to be stored, the measuring being performed using a range sensor facing the first storage; obtaining a storage three-dimensional model that is a three-dimensional model of the first storage in which the measurement target is not stored; extracting a target portion corresponding to the measurement target from the space three-dimensional model using the space three-dimensional model and the storage three-dimensional model; calculating a first three-dimensional coordinate system; estimating a target three-dimensional model using the target portion and the first three-dimensional coordinate system, the target three-dimensional model being a three-dimensional model of the measurement target in the first storage space; and calculating a first filling rate of the measurement target with respect to the first storage space.

A method and an apparatus for training facial feature extraction model, a method and an apparatus for extracting facial features, a device, and a storage medium are provided. The training method includes: inputting face training data into a plurality of original student networks for model training; inputting face verification data into the original student networks; inputting the verified facial feature data into a preset teacher network, respectively; and screening the candidate facial feature data and determining a candidate student network.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.