Samsung Electronics Co., Ltd.

Heungwoo Han

Seongmin Kang

An electronic apparatus includes a camera; a gripper configured to grip a grip target object; a memory configured to store a neural network model; and a processor configured to: obtain movement information and rotation information of the gripper by inputting at least one image captured by the camera to the neural network model, and control the gripper based on the movement information and the rotation information. The at least one image includes at least a part of the gripper and at least a part of the grip target object, and the neural network model is configured to output the movement information and the rotation information for positioning the gripper to be adjacent to the grip target object, based on the at least one image, the movement information includes one of a first direction movement, a second direction movement, or a movement stop of the gripper, and the rotation information includes one of a first direction rotation, a second direction rotation, or a non-rotation of the gripper.

Electronic apparatus and control method thereof

An electronic apparatus may include a processor configured to: obtain an image captured by a camera; obtain a downscaled image by downscaling the captured image, wherein the downscaled image has an image that is less than a critical resolution, identify a region of interest included in the downscaled image by inputting the downscaled image into a first artificial intelligence model, the first artificial intelligence model being trained to identify a region of interest in an image; extract, from the captured image, an object image in the captured image corresponding to the identified region of interest, and obtain information on an object region included in the captured image by inputting the extracted object image into a second artificial intelligence model, the second artificial intelligence model being configured to identify an object region in an input image.

A method of controlling an electronic apparatus includes acquiring an image and depth information of the acquired image; inputting the acquired image into a neural network model trained to acquire information on objects included in the acquired image; acquiring an intermediate feature value output by an intermediate layer of the neural network model; identifying a feature area for at least one object among the objects included in the acquired image based on the intermediate feature value; and acquiring distance information between the electronic apparatus and the at least one object based on the feature area for the at least one object and the depth information.

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Suwon-si, South Korea

Heungwoo Han