The application discloses a lip movement capturing method and device and a storage medium. The method includes: acquiring a real-time image shot by a photographic device and extracting a real-time facial image from the real-time image; inputting the real-time facial image into a pretrained lip average model and recognizing t lip feature points representative of positions of lips in the real-time facial image; and calculating a movement direction and movement distance of the lips in the real-time facial image according to x and y coordinates of the t lip feature points in the real-time facial image. According to the application, movement information of the lips in the real-time facial image is calculated according to the coordinates of the lip feature points to implement real-time capturing of movements of the lips.

A biological image presentation device includes: an acquisition unit; a determination unit determining an image as a standard and an image for comparison; an extraction unit extracting, from the image as a standard, a position where a change of luminance value equal to or larger than a defined value is present; a detection unit detecting a position corresponding to the position extracted from the image for comparison; a division unit dividing the image as a standard on the basis of the position extracted; a mapping unit mapping the image for comparison to an area corresponding to each divided area of the image as a standard while modifying so as to conform to the shape of the divided area; and a display control unit switching and displaying an image for display in a display area by using the image as a standard and an image mapped by the mapping unit.

There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

A method of deep graph representation learning includes: calculating a plurality of base features from a graph and adding the plurality of base features to a feature matrix. The method further includes generating, by a processing device, a current feature layer from the feature matrix and a set of relational feature operators, wherein the current feature layer corresponds to a set of current features, evaluating feature pairs associated with the current feature layer, and selecting a subset of features from the set of current features based on the evaluated feature pairs. The method further includes adding the subset of features to the feature matrix to generate an updated feature matrix.

A three-dimensional (3D) face modeling method and apparatus is disclosed. The 3D face modeling apparatus may generate a personalized 3D face model using a two-dimensional (2D) input image and a generic 3D face model, obtain a depth image and a texture image using the generated personalized 3D face model, determine a patch region of each of the depth image and the texture image, and adjust a shape of the personalized 3D face model based on a matching relationship between the patch region of the depth image and the patch region of the texture image.

A measurement apparatus which includes a processor is provided. The processor is configured to calculate, based on a distance image of a measurement target object with at least a joint, a position of a first portion of the measurement target object which corresponds to a non-joint portion or a terminal portion, and a position of a second portion of the measurement target object different from the first portion, and calculate, based on a first line connecting the calculated positions, a joint angle related to a joint of a first measurement target of the measurement target object.

A relative atlas may be used to lay out elements in a digital map used in the control of an autonomous vehicle. A vehicle pose for the autonomous vehicle within a geographical area may be determined, and the relative atlas may be accessed to identify elements in the geographical area and to determine relative poses between those elements. The elements may then be laid out within the digital map using the determined relative poses, e.g., for use in planning vehicle trajectories, for estimating the states of traffic controls, or for tracking and/or identifying dynamic objects, among other purposes.

A computer-implemented method for detecting a traffic circle on a digital map, the method comprising: detecting a cycle within a road graphic of the digital map; calculating a similarity of an internal angle of a corner of a polygon, which represents the geometry of the cycle, to an internal angle of a corresponding corner of a reference polygon; calculating a similarity indicator on the basis of the calculated similarity of the internal angle of all the corners of the polygon of the detected cycle; and, if the similarity indicator exceeds a predefined threshold value, defining the detected cycle as a traffic circle on the digital map.

Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Methods and systems for identifying biologic subtypes in a biological specimen may include receiving a data set associated with a cohort of biological specimens, determining a potential number of clusters associated with the data set, associating a cluster with one or more data points in the data set, associating a cluster label with the one or more data points in the data set, treating a related cluster as a biologic subtype and associating a biologic subtype with one or more data points with regions of interest in the data set, establishing duster features and metrics in the data set, determining if a new biologic subtype is identified by comparing the biologic subtype associated with the regions of interest with known biologic subtypes, and discovering a new diagnostic, prognostic, predictive, and/or therapeutic biologic subtype by correlating a new biologic subtype with patient, prognostic, predictive, therapeutic and/or clinical trial data.