Samsung Electronics Co., Ltd.

Daehyun Ban

Yongsung Kim

Seongwon Han

Dongwan Lee

Woojin Park

Seowoo Jang

Juyoung Lee

Hongpyo Lee

Chanwon Seo

Hyunsoo Choi

Lei Zhang

Daehun Kim

ChanJu Park

Unkyu Park

An apparatus is provided. The apparatus includes an input/output interface configured to receive an image and output a result, a memory storing one or more instructions for processing the image by using a convolutional neural network, and a processor configured to process the image by executing the one or more instructions, wherein the convolutional neural network (CNN) may include one or more spatial transformation modules, and the spatial transformation module may include a spatial transformer configured to apply a spatial transform to first input data that is the image or an output of a previous spatial transformation module, by using a spatial transformation function, a first convolutional layer configured to perform a convolution operation between the first input data to which the spatial transform is applied and a first filter, and a spatial inverse transformer configured to apply a spatial inverse transform to an output of the first convolutional layer.

Image processing method and apparatus using convolutional neural network

A server for pose estimation of a person and an operating method of the server are provided. The operating method includes obtaining an original image including a person, generating a plurality of input images by rotating the original image, obtaining first pose estimation results respectively corresponding to the plurality of input images, by inputting the plurality of input images to a pose estimation model, applying weights to the first pose estimation results respectively corresponding to the plurality of input images, and obtaining a second pose estimation result, based on the first pose estimation results to which the weights are applied, wherein the first pose estimation results and the second pose estimation result each include data indicating main body parts of the person.

Server for pose estimation and operating method of the server

A method of operating an image processing apparatus is provided. The method includes generating a first feature map by performing a convolution operation between a first image and a first kernel group, generating a second feature map by performing a convolution operation between the first image and a second kernel group, generating a first combination map based on the first feature map, generating a second combination map based on the first feature map and the second feature map, generating a second image based on the first combination map and the second combination map, and generating a reconstructed image of the first image, based on the second image and the first image, and generating a high-resolution image of the first image by inputting the reconstructed image to an upscaling model.

Image processing apparatus and operating method thereof

Disclosed is an electronic device including a learning model having been learned according to an artificial intelligence algorithm. An electronic device according to the present disclosure may comprise: an input unit; and a processor which, when a two-dimensional image including at least one object is received through the input unit, acquires first depth information relating to at least one object by applying the two-dimensional image to a first learning model, acquires second depth information relating to the at least one object by applying the first depth information and actually measured depth data of the at least one object to a second learning model, and acquires three-dimensional information relating to the two-dimensional image on the basis of the second depth information, wherein the first depth information is implemented to include depth data according to a type of the at least one object.

Electronic device and method for controlling same

An electronic apparatus is provided. The electronic apparatus includes: a sensor; a camera; a memory; and a processor configured to be connected to the sensor, the camera, and the memory. The memory includes an artificial intelligence model trained to identify at least one object. The processor is further configured to: detect an object based on sensing data received from the sensor; based on the detected object being identified as having a height less than a predetermined threshold value, warp an object region, including the detected object, in an image acquired through the camera based on distance information of the object region; and identify the detected object by inputting the warped object region into the artificial intelligence model.

Electronic apparatus for identifying object through warped image and control method thereof

An electronic apparatus is provided. The electronic apparatus includes a display, a camera configured to capture a rear of the electronic apparatus facing a front of the electronic apparatus in which the display displays an image, and a processor configured to render a virtual object based on the image captured by the camera, based on a user body being detected from the captured image, estimate a plurality of joint coordinates with respect to the detected user body using a pre-trained learning model, generate an augmented reality image using the estimated plurality of joint coordinates, the rendered virtual object, and the captured image, and control the display to display the generated augmented reality image, wherein the processor is configured to identify whether the user body touches the virtual object based on the plurality of estimated joint coordinates, and change a transmittance of the virtual object based on the touch being identified.

Electronic apparatus and method for controlling thereof

An autonomous driving device is provided. The autonomous driving device includes a driver comprising circuitry configured to drive the autonomous driving device, a sensor, a storage configured to store characteristic information for each type of obstacle, and a processor configured to control the driver based on a result of sensing of the sensor. The processor is configured to, based on an external object sensed by the sensor being recognized as an obstacle with a possibility of collision with the autonomous driving device, identify a type of the obstacle based on the characteristic information, and to control the driver to change at least one of a velocity and direction of the autonomous driving device based on the identified type of obstacle.

Autonomous driving device and driving method thereof

Disclosed are an autonomous driving apparatus for performing autonomous driving of a vehicle and a controlling method thereof. An autonomous driving apparatus according to an example aspect of the present disclosure includes a sensor configured to acquire sensing information to determine a driving state of the vehicle; a storage configured to store a plurality of autonomous driving models; and at least one processor configured to perform autonomous driving of the vehicle using one of the plurality of autonomous driving models stored in the storage based on sensing information sensed by the sensor. Accordingly, the autonomous driving apparatus is capable of performing autonomous driving of a vehicle by rapidly changing a driving mode to an autonomous driving mode suitable for an event occurring during autonomous driving of the vehicle.

Autonomous driving apparatus and method for autonomous driving of a vehicle

A method for controlling an autonomous driving apparatus of a vehicle includes receiving a destination for setting a route, obtaining driving history of a user and traffic information, determining a driving route to the destination based on information on the destination, information on the driving history, and the traffic information provided to a model trained through an artificial intelligence algorithm as input data, and performing autonomous driving along the determined driving route.

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Suwon-si, South Korea

Daehyun Ban