A method, apparatus, and computer-readable medium for confirming a perceived position of a traffic light, by obtaining identifiers and results of a first perception of traffic lights associated with the identifiers, the results of the first perception including a first estimation of an ellipse encompassing each of the traffic lights, receiving results of a second perception of traffic lights associated with the identifiers, the results of the second perception including a second estimation of an ellipse encompassing each of the traffic lights, calculating, based on the first perception and the second perception, association parameters for each possible pair of estimated ellipses, selecting, based on the calculated association parameters for each possible pair of estimated ellipses, matching pairs of estimated ellipses, and fusing each matching pair of estimated ellipses.

A method, apparatus, and computer-readable medium for confirming a perceived position of a traffic light, by obtaining identifiers and results of a first perception of traffic lights associated with the identifiers, the results of the first perception including a first estimation of an ellipse encompassing each of the traffic lights, receiving results of a second perception of traffic lights associated with the identifiers, the results of the second perception including a second estimation of an ellipse encompassing each of the traffic lights, calculating, based on the first perception and the second perception, association parameters for each possible pair of estimated ellipses, selecting, based on the calculated association parameters for each possible pair of estimated ellipses, matching pairs of estimated ellipses, and fusing each matching pair of estimated ellipses.

Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging. The method includes identifying at least one quantitative measurement of a subject using ultrasound imaging, the at least one quantitative measurement included as part of quantitative information of the subject gathered based on the ultrasound imaging, comparing the at least one quantitative measurement to a first predetermined standard to determine a first initial value, the first predetermined standard falling within a first range of quantities, identifying at least one qualitative measurement of the subject using the ultrasound imaging, the at least one qualitative measurement included as part of qualitative information of the subject gathered based on the ultrasound imaging, comparing the at least one qualitative measurement to a second predetermined standard to determine a second initial value, the second predetermined standard falling within a second range of quantities; and correlating at least the quantitative information and the qualitative information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

The present disclosure provides a dance animation processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring multiple dance action segments, and establishing an animation state transition relationship for the multiple dance action segments, each action node in the animation state transition relationship corresponding to one dance action segment, and a transition cost existing among the action nodes; acquiring a target audio file, and determining a music feature sequence for the target audio file; determining a dance action sequence for the music feature sequence according to the transition cost in the animation state transition relationship; and generating a dance animation for the target audio file according to the dance action sequence.

Predictive analytics techniques are provided for produce leading indicators of economic activity based on observed pedestrian attributes—e.g., appearance and behavior—and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

Predictive analytics techniques are provided for produce leading indicators of economic activity based on observed pedestrian attributes—e.g., appearance and behavior—and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

An approach is provided for linearizing a network of features for machine learning tasks. The approach involves, for instance, receiving a graph representation of a network of a plurality of features. For example, a plurality of vertices of the graph representation, an edge connecting two vertices of the plurality of vertices, or a combination thereof respectively represents the plurality of features. The approach also involves determining a linear order of the plurality of features based on a selected criterion. The approach further involves generating a vector representation of the plurality of features based on the linear order. The approach further involves using the vector representation as an input, an output, or a combination thereof of a machine learning model.

Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself—rather than using a priori definitions for what should constitute a measurable unit of action—identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

A fingerprint verification method and apparatus is disclosed. The fingerprint verification method may include obtaining an input fingerprint image, determining a matching region between the input fingerprint image and a registered fingerprint image, determining a similarity corresponding to the matching region, representing a determined indication of similarities between the input fingerprint image and the registered fingerprint image, relating the determined similarity to the matching region as represented in a matching region-based similarity, determining a result of a verification of the input fingerprint image based on the matching region-based similarity, and indicating the result of the verification.