The present invention relates to a method, a device, and a system for providing fashion information. Particularly, the system for providing fashion information, of the present invention, comprises: a sample data generation unit for generating sample data in which the same fashion items of various sizes are matched according to human body information; a sample data storage unit for storing the sample data; and a predictive fit data providing unit for receiving, from a user device, fashion item information and user body information, and generating predictive fit data in reference to the sample data stored in the sample data storage unit, wherein the predictive fit data can be referred to size or fit when a user selects the fashion items.

Embodiments of the present disclosure describe an electronic device for providing a visual effect corresponding to a gesture input. The electronic device includes a sensor, a wireless communication circuit, a display device, and at least one processor. The at least one processor is configured to sense a gesture input through the sensor. The at least one processor is also configured to control the display device to display a first visual effect corresponding to the gesture input. When information related to the first visual effect is received from another electronic device through the wireless communication circuit, the at least one processor is configured to update the first visual effect displayed on the display device to a second visual effect.

An embodiment of this application discloses a three-dimensional reconstruction method. The method in this embodiment of this application includes: obtaining an image of a first object and a camera pose of the image; determining a first normalized object location field NOLF image of the first object in the image by using a first deep learning network, where the first NOLF image indicates a normalized three-dimensional point cloud of the first object at a photographing angle of view of the image; determining, from a plurality of three-dimensional models in a model database based on the first NOLF image, a first model corresponding to the first object; determining a pose of the first object based on the first model and the camera pose of the image; and performing three-dimensional reconstruction on the first object in the image based on the first model and the pose of the first object.

Provided is a method for processing images, including: determining a target processing region in a target image based on facial key points; acquiring a low-and-mid-frequency image and a low-frequency image corresponding to the target image by filtering the target image; acquiring a first image by adjusting pixel values of pixel points in the target processing region in the low-and-mid-frequency image based on differences between the pixel values of the pixel points in the target processing region in the low-frequency image and pixel values of pixel points at corresponding positions in the low-and-mid-frequency image; and acquiring a second image by adjusting pixel values of pixel points in the target processing region in the first image based on differences between pixel values of pixel points in the target processing region in the target image and the pixel values of the pixel points at corresponding positions in the low-and-mid-frequency image.

A sensor device, an electronic apparatus and a method for reducing signal noise are provided. The sensor device includes a first detection region and a second detection region. The first detection region includes at least one detector unit, the detector unit includes a first detection electrode and a second detection electrode opposed to each other and a first insulating layer, the first detection electrode is electrically insulated from the second detection electrode by the first insulating layer the second detection region includes at least one detector unit, the sensor unit includes a first sensor electrode, a second sensor electrode and a first photosensitive layer, and the first photosensitive layer is electrically connected to the first sensor electrode and the second sensor electrode.

In some embodiments, a security device comprises a context information intake device, a sensor, and a controller. The controller is configured to activate the context information intake device when the sensor detects an activation event initiated by an object such that the context information intake device records context information associated with the object; and perform a context-aware cryptographic process to cryptographically authenticate the object using attribute-based access control based on the recorded context information associated with the object.

The present invention relates to a method and system for automatic localisation of static objects in an urban environment. More particularly, the present invention relates to the use of noisy 2-Dimensional (2D) image data to identify and determine 3-Dimensional (3D) positions of objects in large scale urban or city environments. Aspects and/or embodiments seek to provide a method, system, and vehicle for automatically locating static 3D objects in urban environments by using a voting-based triangulation technique. Aspects and/or embodiments also provide a method for updating map data after automatically new 3D static objects in an environment.

The present invention provides a data collection system comprising: a camera; a location module; a plurality of sensors; and a first processor communicatively coupled to the camera and the location module, the first processor programmed to: obtain a plurality of frames from the camera; obtain a plurality of locations from the location module; obtain a plurality of data measurements from the plurality of sensors; apply a previously trained first neural network model for identifying problematic road segments to frames captured by the camera; and if the first neural network model indicates that a frame is a problematic road segment, save the frame in association with a location provided by the location module.

A method includes a computing system accessing a training sample that includes first sensor data obtained using a first sensor at a first geographic location, and first metadata comprising information relating to the first sensor. The system may train a machine-learning model by generating first map data by processing the training sample using the model and updating the model based on the generated first map data and target map data associated with the first geographic location. The system may then access second sensor data and second metadata, where the second sensor data is obtained using a second sensor. The system may generate second map data associated with a second geographic location by processing the second sensor data and the second metadata using the trained model. A high-definition map may be generated using the second map data.

A generation device executes an inference process of inputting analysis target data to a plurality of inference models and outputting a plurality of inference results related to an object included in the analysis target data from the plurality of inference models, a determination process of determining, based on the plurality of inference results, a specific inference result from the plurality of inference results output by the inference process, and a generation process of generating a training data set including the specific inference result determined by the determination process and the analysis target data.