Systems and methods permit generation of a digital scan of a user's face such as for obtaining of a patient respiratory mask, or component(s) thereof, based on the digital scan. The method may include: receiving video data comprising a plurality of video frames of the user's face taken from a plurality of angles relative to the user's face, generating a three-dimensional representation of a surface of the user's face based on the plurality of video frames, receiving scale estimation data associated with the received video data, the scale estimation data indicative of a relative size of the user's face, and scaling the digital three-dimensional representation of the user's face based on the scale estimation data. In some aspects, the scale estimation data may be derived from motion information collected by the same device that collects the scan of the user's face.

A method configured to implemented on at least one image processing device for detecting objects in images includes obtaining an image including an object. The method also includes generating one or more feature vectors related to the image based on a first convolutional neural network, wherein the one or more feature vectors includes a plurality of parameters. The method further includes determining the position of the object based on at least one of the plurality of parameters. The method still further includes determining a category associated with the object based on at least one the plurality of parameters.

A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the image into a region of interest that includes information useful for classification and a background region by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.

Data processing systems and methods for authenticating users and for generating user authentication indications is disclosed. In one embodiment, a data processing system for authenticating a user, comprises: a computer processor and a data storage device, the data storage device storing instructions operative by the processor to: receive a user indication identifying a user; receive an authentication indication for the user, the authentication indication comprising a sequence of word-gesture pair indications, each word-gesture pair indication comprising a word indication and a gesture indication; look up a stored authentication indication for the user; compare the received authentication indication with the stored authentication indication; and generate an authentication result indication indicating the result of the comparison.

A centrifugal protective hair blower, comprising a plastic shell for wrapping powered devices of the blower, wherein the plastic shell is made of polycarbonate casting; an electric heating device which is arranged in the plastic shell of the hair blower, composed of a two to three sections electric heating wire, and is used to control the blowing temperature of the hair blower; a hemispherical picture-shooting mechanism, which includes a trailing measurement device, an image interception device, a data analysis device, a DC drive motor, an optical filter, optical lens and an image sensing device; an electric heating device, which is connected with a vector analysis device, and is used to reduce the blowing temperature of the hair blower when the target type is a cat, a dog or a rabbit. Through the present invention, it is capable of preventing the hair blower from causing unwarranted injury to surrounding pets.

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

A method for predicting a face beauty grade includes the following steps of: acquiring a beautiful face image of a face beauty database, preprocessing the beautiful face image, and extracting a beauty feature vector of the beautiful face image, the preprocessing unifying data of the beautiful face image; recognizing continuous features of samples of the same type in a feature space by using a bionic pattern recognition model, and classifying the beauty feature vector to obtain a face beauty grade prediction model; and collecting a face image to be recognized, and inputting the face image to be recognized into the face beauty grade prediction model to predict a face beauty grade and obtain the beauty grade of the face image to be recognized.

Techniques and systems are provided for determining features for one or more objects in one or more video frames. For example, an image of an object, such as a face, can be received, and features of the object in the image can be identified. A size of the object can be determined based on the image, for example based on inter-eye distance of a face. Based on the size, either a high-resolution set of features or a low-resolution set of features is selected to compare to the features of the object. The object can be identified by matching the features of the object to matching features from the selected set of features.

The present invention provides a method for reconstructing a 3D object based on dynamic graph network, first, obtaining a plurality of feature vectors from 2D image I of an object; then, preparing input data: predefining an initial ellipsoid mesh, obtaining a feature input X by filling initial features and creating a relationship matrix A corresponding to the feature input X; then, inputting the feature input X and corresponding relationship matrix A to a dynamic graph network for integrating and deducing of each vertex's feature, thus new relationship matrix is obtained and used for the later graph convoluting, which improves the initial graph information and makes the initial graph information adapted to the mesh relation of the corresponding object, therefore the accuracy and the effect of 3D object reconstruction have been improved; last, regressing the position, thus the 3D structure of the object is deduced, and the 3D object reconstruction is completed.

Systems and processes can reduce an amount of training data used to generate a machine learning model while maintaining or improving a resultant of the machine learning model. The amount of training data may be reduced by pre-processing the training data to normalize the training data. The training data may include images of portions of an elongated object, such as a road. Each of the images can be normalized by, for example, rotating each of the images such that the depicted roads are horizontal or otherwise share the same angle. By aligning disparate images of roads, it is possible to reduce the amount of training data and to increase the accuracy of the machine learning model. Further, the use of normalized images by the machine learning model enables a reduction in computing resources used to apply data to the machine learning model to, for example, identify lane markings within images.