Abstract:
A processor-implemented method of tracking a target object includes: extracting a feature from frames of an input image; selecting one a neural network model from among a plurality of neural network models that is provided in advance based on a feature value range, based on a feature value of a target object that is included in the feature of a previous frame among the frames; and generating a bounding box of the target object included in a current frame among the frames, based on the selected neural network model.
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:
Example embodiments disclose a method of generating a feature vector, a method of generating a histogram, a learning unit classifier, a recognition apparatus, and a detection apparatus, in which a feature point is detected from an input image based on a dominant direction analysis of a gradient distribution, and a feature vector corresponding to the detected feature point is generated.
Abstract:
An estimator training method and a pose estimating method using a depth image are disclosed, in which the estimator training method may train an estimator configured to estimate a pose of an object, based on an association between synthetic data and real data, and the pose estimating method may estimate the pose of the object using the trained estimator.
Abstract:
A target object detection method and apparatus are provided. The target object detection method and apparatus are applicable to fields such as artificial intelligence, object tracking, object detection, and image processing. An object is detected from a frame image of a video including a plurality of frame images based on a target template set including one or more target templates.
Abstract:
A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.
Abstract:
A depth estimation method and apparatus are provided. The depth estimation method includes obtaining an image from an image sensor comprising upper pixels, each comprising N sub-pixels, obtaining N sub-images respectively corresponding to the N sub-pixels from the image, obtaining a viewpoint difference between the N sub-images using a first neural network, and obtaining a depth map of the image based on the viewpoint difference using a second neural network.
Abstract:
A processor-implemented recognition method includes: receiving query input data; determining a domain to which the query input data belongs using a neural network-based classifier; and in response to the query input data belonging to a first domain, generating second query data of a second domain based on the query input data.
Abstract:
A method and apparatus with emotion recognition acquires a plurality of pieces of data corresponding a plurality of inputs for each modality and corresponding to a plurality of modalities; determines a dynamics representation vector corresponding to each of the plurality of modalities based on a plurality of features for each modality extracted from the plurality of pieces of data; determines a fused representation vector based on the plurality of dynamics representation vectors corresponding to the plurality of modalities; and recognizes an emotion of a user based on the fused representation vector.
Abstract:
A method and apparatus for estimating a pose that estimates a pose of a user using a depth image is provided, the method including, recognizing a pose of a user from a depth image, and tracking the pose of the user using a user model exclusively of one another to enhance precision of estimating the pose.