Samsung Electronics Co., Ltd.

Hyundai Motor Company

Industry-university Cooperation Foundation Korea Aerospace University

Kia Motors Corporation

Jinyoung Hwang

Dongnam Byun

Dongchan Kim

Chan Kwak

Jong Wook Roh

Haedong Yeo

Jongmyeong Ban

Jaeyeop Lee

Hyechan Bae

Sungwoo Hwang

Jongmin Lee

Doh Won Jung

Se Yun Kim

Yun Sung Woo

Seongwo Oh

Jae-young Choi

Hyeon Cheol Park

Mi Jeong Kim

Weonho Shin

Hye Ran Kim

Seongwoo Oh

Eun Joo Jang

Jong Soo Kim

Jong Sool Park

Jin Ho Kim

JongSool Park

Tae Gon Kim

Jooho Lee

Kwanghee Kim

Changseung Lee

Chung Kun Cho

Hoondo Heo

Daejin Yang

Kwang Hee Kim

Hyangsook Lee

Ginam Kim

Jaekwan Kim

Jaewook Shin

Hyosug Lee

Eunhyoung Cho

Bongjun Ko

Sang Bum Baek

Sanghun Kwak

Inyong Song

Weon Ho Shin

Wonjune Sung

Jae-Young Choi

Yeonggyo Yoon

Jongmin Wi

Provided is an electronic device configured to perform an operation corresponding to a user input by using an artificial intelligence model, and a method performed by the electronic device, of performing the operation. The method comprises obtaining touch data related to a touch input of a user, identifying a first training data set similar to the touch data from a plurality of pre-stored training data sets, training an artificial intelligence model based on the first training data set, identifying a type of a user input that is input to the electronic device, based on the trained artificial intelligence model, and performing an operation corresponding to the identified type of the user input.

Personalized electronic device inferring user input, and method for controlling same

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

A method performed by an electronic device, includes: A method for performed by an electronic device, includes: acquiring an image by using a first lens; inputting information about a plurality of candidate regions of the image into a neural network model that is trained to acquire a score indicating whether a face of a person is included, and acquiring scores for the plurality of candidate regions; identifying a candidate region including at least part of a person's face among the plurality of candidate regions based on the acquired scores; and providing guide information for changing a capturing composition based on information about the location of the identified person region and a score of the identified person region.

Electronic device and method for controlling the electronic device

An electronic apparatus includes a camera including a first lens and a second lens capable of obtaining an image having an angle of view different from the first lens, a display, a memory, and a processor. To perform a controlling method, the processor is configured to provide a first image obtained using the first lens to the display as a live view, obtain a second image using the second lens, obtain image information regarding the second image and object information regarding at least one object depicted in the second image using at least one neural network model, identify a screen type of the second image based on the image information and the object information, identify a set of filters corresponding to the screen type of the second image, and correct the first image based on the identified set of filters to provide a corrected first image as the live view.

Electronic apparatus and control method thereof

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

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

An electronic device includes a touch screen and a processor configured to: based on a touch input of a user being acquired through the touch screen, acquire an image corresponding to the acquired touch input of the user; identify a type of the acquired touch input of the user by inputting, to a neural network model for identifying the type of the touch input of the user, the acquired image, a first image corresponding to a first type touch input obtained by touching the touch screen with a pressure smaller than a preconfigured pressure, and a second image corresponding to a second type touch input obtained by touching the touch screen with a pressure greater than the preconfigured pressure; and perform a function based on the identified type of the touch input.

Device and method with trained neural network to identify touch input

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.

Electronic device and operating method of the same

An optical filter includes a near-infrared absorbing layer including a first material, the first material being configured to absorb light in a first wavelength spectrum belonging to a near-infrared wavelength spectrum. The optical filter includes a compensation layer adjacent to the near-infrared absorbing layer, the compensation layer including a second material different from the first material. The optical filter includes a metamaterial structure spaced apart from the near-infrared absorbing layer via the compensation layer, the metamaterial structure being configured to absorb or reflect light in a second wavelength spectrum at least partially overlapped with the first wavelength spectrum.

Optical filters and image sensors and camera modules and electronic devices

An optical filter includes a light absorbing layer and a conductive nanodisk. The light absorbing layer includes a near-infrared absorbing material configured to absorb light of a first wavelength spectrum within a near-infrared wavelength spectrum. The conductive nanodisk is configured to absorb or reflect light of a second wavelength spectrum within the first wavelength spectrum. An image sensor includes the optical filter, a camera module includes the optical filter, and an electronic device includes the optical filter.

A device is provided. The device includes a processor and a memory configured to store instructions executable by the processor. The processor is configured to execute the instructions to extract context information from displayed data based on an application which is being executed by the device, identify an identifier from the context information, search for at least one recommended contact related to the identifier based on the identifier and a relation graph obtained by inputting information regarding a communication between a plurality of users into a first training model for determining an association between the plurality of users, identify a priority of the at least one recommended contact, and control to display the at least one recommended contact according to the priority.

Device and method for recommending contact information

An electrode structure includes: a first nonconductive layer; a first conductive layer disposed on the first nonconductive layer; a second nonconductive layer disposed on the first conductive layer; a second conductive layer disposed on the second nonconductive layer; and a third nonconductive layer disposed on the second conductive layer, where at least one of the first conductive layer and the second conductive layer includes a two-dimensional conductive material.

Electrode structure and touch detecting sensor using the same

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Yongin-si, South Korea

Jinyoung Hwang