Abstract:
A processor-implemented method includes: generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.
Abstract:
A user trigger intent determining method and apparatus is disclosed. The user trigger intent determining apparatus may obtain a first face image, obtain a second face image after a visual stimuli object is displayed, and determine a final gaze location by correcting a first gaze location estimated from the first face image based on a second gaze location estimated from the second face image.
Abstract:
An interactive method includes displaying image content received through a television (TV) network, identifying an object of interest of a user among a plurality of regions or a plurality of objects included in the image content, and providing additional information corresponding to the object of interest.
Abstract:
A three-dimensional (3D) display device for providing an input-output interface using a dynamic magnetic field control is disclosed, the device including a display unit to display a 3D image, a magnetic field generation unit to generate a magnetic field, and a control unit to dynamically control the magnetic field generation unit to generate a 3D magnetic field associated with the 3D image.
Abstract:
An image processing method is provided. The method includes obtaining a first bird's eye view (BEV) feature of first data, obtaining a second BEV feature of second data, inputting the first BEV feature and the second BEV feature to a self-attention network to obtain a third BEV feature of the first data and a fourth BEV feature of the second data, and obtaining a fusion feature in which the third BEV feature and the fourth BEV feature are fused, wherein the first data and the second data are collected by different sensors. Related operations of the examples provided by the present disclosure are implemented by an AI model. AI-related functions are performed by non-volatile memory, volatile memory, and a processor.
Abstract:
A processor-implemented method with visual code processing includes generating a first image by capturing a first visual code, using an always on (AO) sensor module of an electronic device, detecting a first code image included in the first image and corresponding to the first visual code, using the AO sensor module, performing a first type decoding on the first code image, using the AO sensor module, waking up an application processor (AP) of the electronic device from a low-power state, based on a first decoding result of the first type decoding, and executing a first application of the electronic device corresponding to the first visual code, using the AP.
Abstract:
A processor-implemented method with target object tracking includes: setting a search area for a target object included in an input image based on a position of a first target box in a template image; selecting a network path from a plurality of network paths of a neural network model according to a resizing ratio of a size of another image that is input to the neural network model to a size of the search area; and tracking the target object by estimating a position of a second target box corresponding to the target object in the input image according to the selected network path.
Abstract:
An apparatus with image segmentation includes: one or more processors configured to: obtain a second image based on a first segmentation label corresponding to a first image; generate a composite image by composing an image of an object corresponding to an object class of the first image among objects comprised by the second image with the first image; and train an image segmentation model based on the composite image, the first image, and the second image.
Abstract:
A method of generating a hyperlapse video includes: comparing a first reference point of a first image and a corresponding second reference point of a second image; based on the comparing, displaying a first user interface for matching the first reference point and second reference point; and determining whether to perform automatic shooting for the hyperlapse video based on whether the first reference point and the second reference point match.
Abstract:
A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image