Disclosed is a system and method for segmentation of selected data. In various embodiments, automatic segmentation of fiber tracts in an image data may be performed. The automatic segmentation may allow for identification of specific fiber tracts in an image.

Disclosed is a system and method for segmentation of selected data. In various embodiments, automatic segmentation of fiber tracts in an image data may be performed. The automatic segmentation may allow for identification of specific fiber tracts in an image.

Disclosed is a system and method for segmentation of selected data. In various embodiments, automatic segmentation of fiber tracts in an image data may be performed. The automatic segmentation may allow for identification of specific fiber tracts in an image.

A system may be configured to conflate vectorized source data. Some embodiments may: obtain first data from a first source and second data from a second source; determine a first polygon that encloses all features of the first data and a second polygon that encloses all features of the second data; determine a larger polygon that encloses the first and second polygons; divide the larger polygon into first tiles; extract, from each of the first tiles overlaying the first data and from the each tile overlaying the second data, a first set of features and a second set of features, respectively; and identify, based on a computed disagreement level satisfying a set of criteria, each of one or more of the tiles. A set of identified tiles or all of the tiles may then be displayed, including shaded indicators overlaying features of respective portions of the first and second data.

A smart city management system may enable creating a digital twin of the smart city based on mapping lidar data for the smart city and radio frequency data for the smart city; determining placement of a set of network devices in the smart city based on the created digital twin; and providing a visualization of the determined placement of the set of network devices.

A method of generating a virtual idol is provided. The method includes: during obtaining of a virtual idol, feature information of a target object may be obtained, and then the feature information of the target object is used as a generation basis for the virtual idol, so that a target virtual face matching with the feature information may be determined from a preset face material library in a targeted manner, and a target motion video matching with the feature information may be determined from a preset motion video library; and then the virtual face and a facial image in the motion video are fused, which may generate the virtual idol for a scenario where the target object is endorsed, so that the virtual idol may subsequently be used to endorse the target object.

The disclosure herein describes triggering image capture requests at ground Internet-of-Things (IoT) devices based on sensor events, and processing the image capture requests by satellites. An image capture request is received by the satellite from a ground IoT device. An image capture request includes request type data, location data, and response target data. The request type data is indicative of a sensor event at the ground IoT device. Image data is captured using an image capture device of the satellite. The image data is of an area based on the location data. A response to the request is generated based on the captured image data and the request type data, and the generated response is sent to the response target based on the response target data of the image capture request. The disclosure enables satellites to efficiently respond to a wide variety of image capture requests from ground IoT devices.

A method for motion prediction includes receiving spatial information output by a radio-wave sensor, wherein the spatial information includes position and velocity of at least one point; receiving an image captured by a camera; tracking at least one object based on the spatial information and the image to obtain a consolidated tracking result; predicting a motion trajectory of the at least one object based on the consolidated tracking result to obtain a prediction result; and controlling the camera according to the prediction result.

According to the present disclosure, a method performed by an electronic device may include: obtaining image data using an always-on camera, obtaining sensor data, obtaining combined data from the image data and the sensor data based on an obtained time of the image data and an obtained time of the sensor data, extracting at least one feature based on the combined data, generating and storing at least one feature set based on the at least one feature, performing clustering on the at least one feature set, and storing a result of performing the clustering.

The present invention provides a method of generating a robust global map using a plurality of limited field-of-view cameras to capture an environment.

Provided is a method for generating a three-dimensional map comprising: receiving a plurality of sequential image data wherein each of the plurality of sequential image data comprises a plurality of sequential images, further wherein the plurality of sequential images is obtained by a plurality of limited field-of-view image sensors; determining a pose of each of the plurality of sequential images of each of the plurality of sequential image data; determining one or more overlapping poses using the determined poses of the sequential image data; selecting at least one set of images from the plurality of sequential images wherein each set of images are determined to have overlapping poses; and constructing one or more map portions derived from each of the at least one set of images.