Abstract:
A method, an apparatus, an electronic device for estimating a pose of an object include determining a confidence of a depth image of an object based on a color image and the depth image of the object, estimating a pose of the object based on a three-dimensional (3D) keypoint in response to the depth image being reliable, and estimating the pose of the object based on a two-dimensional (2D) keypoint in response to the depth image being unreliable.
Abstract:
The method of the present invention for processing a video comprises: acquiring a first and a second omnidirectional videos having a stereoscopic parallax in a first direction which is a corresponding column direction when the first and the second omnidirectional videos are unfolded by longitude and latitude; and, determining one or two third omnidirectional videos according to the first and the second omnidirectional videos, the second and the third omnidirectional videos having a stereoscopic parallax in a second direction, wherein, if one third omnidirectional video is determined, the second and the third omnidirectional videos have a stereoscopic parallax in the second direction; if two third omnidirectional videos are determined, the two third omnidirectional videos have a stereoscopic parallax in the second direction; and, the second direction is a corresponding row direction when the first and the second omnidirectional videos are unfolded by longitude and latitude.
Abstract:
Provided is a method and apparatus for aligning a three-dimensional (3D) model. The 3D model alignment method includes acquiring, by a processor, at least one two-dimensional (2D) image including an object, detecting, by the processor, a feature point of the object in the at least one 2D input image using a neural network, estimating, by the processor, a 3D pose of the object in the at least one 2D input image using the neural network, retrieving, by the processor, a target 3D model based on the estimated 3D pose, and aligning, by the processor, the target 3D model and the object based on the feature point.
Abstract:
Disclosed are a pose estimation methods and apparatuses of displaying a virtual object using an estimated pose. The pose estimation method includes receiving an input image and estimating pose information of an object from the input image based on local information of the object.
Abstract:
A method and apparatus to authenticate a registered user are described. The method and apparatus include a processor configured to identify a first electrocardiogram (ECG) signal measured from the user, and determine a similarity between the first ECG signal and a second ECG signal based on the identified first ECG signal and the second ECG signal included in a reference ECG signal set. The processor is also configured to determine an authentication threshold corresponding to the reference ECG signal set, and determine whether to authenticate the first ECG signal measured from the user by comparing the determined similarity and the authentication threshold.
Abstract:
Disclosed are an electrocardiogram (ECG) authentication method and apparatus, and a training method and apparatus for training a neural network model used for ECG authentication, the ECG authentication apparatus being configured to acquire an ECG signal of a subject, extract a semantic feature of the ECG signal, and authenticate the subject based on the extracted semantic feature.
Abstract:
A method with object pose estimation includes: obtaining an instance segmentation image and a normalized object coordinate space (NOCS) map by processing an input single-frame image using a deep neural network (DNN); obtaining a two-dimensional and three-dimensional (2D-3D) mapping relationship based on the instance segmentation image and the NOCS map; and determining a pose of an object instance in the input single-frame image based on the 2D-3D mapping relationship.
Abstract:
An apparatus for recommending a customer item identifies a purchase tendency of a customer based on an image, determines a recommended item for the customer by selecting a purchase tendency model corresponding to the purchase tendency, and provides information associated with the determined recommended item.
Abstract:
A user interface method and apparatus are provided. A user interface method of a smart device includes receiving an electrical signal from an electromyography (EMG) sensor worn on a user, determining a user input based on the received electrical signal and position information about a determined position of a detected touch of the user on a touch screen, and controlling operation of the smart device in response to the determined user input.
Abstract:
Provided are electrocardiogram (ECG)-based authentication and training. An authentication method includes generating a feature vector of an ECG obtained from an entity or a person based on a dictionary, classifying the ECG through a classifier based on the feature vector, and performing authentication based on a classification result.