Samsung Electronics Co., Ltd.

Dongnam Byun

Jinyoung Hwang

Haedong Yeo

Dongchan Kim

Jongmyeong Ban

Hyechan Bae

Jaeyeop Lee

Seongwo Oh

Hyewon Kim

Hoondo Heo

Hyunhan Kim

Jaewook Shin

Bongjun Ko

Sanghun Kwak

Yujin Jeong

Seongwoo Oh

Seohee Kim

Jongmin Wi

A method of generating a three-dimensional (3D) model and a device for generating a 3D model are provided. The method includes generating a base 3D model of an object, determining a first target 3D model similar to the base 3D model from a 3D model database storing a plurality of 3D models, generating a first synthesized 3D model, determining a first similarity between the first synthesized 3D model and the at least one object image, determining a component having a low similarity between the first synthesized 3D model and the at least one object image, determining, as a second target 3D model, a 3D model having a high similarity to the first synthesized 3D model with respect to the component having the low similarity, from the 3D model database, and generating a second synthesized 3D model.

Method and electronic device for generating three-dimensional (3D) model

An electronic device is provide, the electronic device including: a communication interface including at least one circuit; a memory including at least one instruction; and a processor. The processor is configured to: obtain a plurality of images, wherein the plurality of images include an one or more objects; obtain, by inputting the plurality of photographed images into a first neural network model for identifying objects: a feature value for each object of the one or more objects, a predicted class for each object of the one or more objects based on the respective obtained feature values, and a probability value for the predicted class for each of the one or more objects; identify an one or more learning images among the plurality of images based on the obtained probability values; identify one or more clusters of feature values by mapping the feature values of the one or more objects included in the one or more identified learning images to a vector space; obtain a learning data from the one or more identified learning images based on the obtained feature values; transmit the obtained learning data to an external device through the communication interface; receive an information on a second neural network model from the external device, and update the first neural network model based on the received information on the second neural network model.

Electronic device and control method thereof

A control method for an electronic device is disclosed. The control method comprises the steps of during a call with a user of another electronic device by means of an electronic device, acquiring at least one keyword from a content of the call with the user of the another electronic device displaying the at least one keyword during the call and providing a search result for a keyword selected by a user from among the displayed at least one keyword. Particularly, at least a part of the control method of the present disclosure may use an artificial intelligence model learned according to at least one of machine learning, a neural network, and a deep learning algorithm.

Electronic device and control method therefor

An electronic device and a controlling method of the electronic device are provided. The controlling method of an electronic device according to the disclosure includes the steps of, based on a first user input for or related to acquiring a live view image through a camera including a plurality of lenses different from one another being received, acquiring a plurality of image frames for each of the plurality of lenses and storing the image frames in a first memory, inputting the plurality of image frames for each lens stored in the first memory into a neural network model, by a predetermined time interval, and acquiring score information including composition preference information of each of the input image frames, selecting at least one lens among the plurality of lenses based on the score information, storing image frames acquired through the selected at least one lens in a second memory during the predetermined time interval, and based on a second user input for initiating recording of the live view image being received, storing an image related to the image frames stored in the second memory in a third memory until a time point when a third user input for ending the recording is received.

Electronic device and controlling method of electronic device

An electronic device is provided. The electronic device includes a display, a touch sensor disposed in the display and including a plurality of lines and a plurality of nodes formed by the plurality of lines, a memory storing instructions, and a processor. The instructions, when being executed by the processor, cause the electronic device to obtain a plurality of node values from each of the plurality of nodes, change the touch sensitivity of the touch sensor from a first touch sensitivity to a second touch sensitivity lower than the first touch sensitivity based on identifying a pattern by the plurality of node values, identify whether to recognize contact on the touch sensor as a touch input through the plurality of line values obtained from each of the plurality of lines based on identifying changes of the pattern by the plurality of node values.

Electronic device sensing contact of fluid through touch sensor

Provided is an electronic apparatus, including: a sensor; a camera; a storage configured to store first and second artificial intelligence models; a first processor; and a second processor. The second processor is configured to: input an image obtained through the camera to the first artificial intelligence model and identify the object of the first type in the image, and input the image obtained through the camera to the second artificial intelligence model and identify the object of the second type, at least a part of which is not viewable in the image. The first processor is configured to: determine a distance between the object of the first type and the object of the second type based on sensing data received from the sensor; and control the electronic apparatus to travel based on the determined distance.

Electronic apparatus and controlling method thereof

An electronic device and a control method thereof are provided. The control method of an electronic device includes the steps of when a user command for initiating video recording is received, transmitting, to an external device connected to the electronic device, a first signal including a request for initiating video recording, acquiring a first image according to the user command, acquiring a first score for composition of the first image by using a trained neural network model, when a second signal including a second score for composition of a second image acquired by the external device is received according to the first signal, identifying, from the second image, at least one merging section to be merged into the first image, on the basis of the first score and the second score, transmitting, to the external device, a third signal including a request for image data corresponding to the at least one merging section, and when a fourth signal including the image data is received according to the third signal, acquiring a third image by merging an image corresponding to the at least one merging section into the first image on the basis of the image data.

Electronic device and control method of electronic device

Provided is a method of identifying a touch type of a user touch input with respect to an electronic device, the method including: obtaining touch data from a touch input received from a user; determining a touch recognition model set consisting of touch recognition models to be used to identify a touch type of the touch input of the user from among a plurality of touch recognition models corresponding to a plurality of partial time periods included in a time in which the touch input is maintained; obtaining touch type probability values with respect to the touch input of the user by applying the touch data to the touch recognition models included in the touch recognition model set; and identifying a touch type of the touch input, based on the obtained touch type probability values.

Growing community of inventors

Suwon-si, South Korea

Dongnam Byun