Abstract: A method of modeling a structure of a coronary artery of a subject may include: forming a learning-based shape model of the structure of the artery, based on positions of landmarks acquired from three-dimensional images; receiving a target image; and/or modeling the artery structure included in the target image, using the model. An apparatus for modeling a structure of a coronary artery may include: a memory configured to store a learning-based shape model of the artery, the learning-based shape model being formed based on positions of a plurality of landmarks acquired from three-dimensional images, the plurality of the landmarks corresponding to the artery; a communication circuit configured to receive a target image; and/or a processing circuit configured to model the artery structure included in the target image, using the model.

Abstract: A multi-lens based capturing apparatus and method are provided. The capturing apparatus includes a lens array including lenses and a sensor including sensing pixels, wherein at least a portion of sensing pixels in the sensor may generate sensing information based on light entering through different lenses in the lens array, and light incident on each sensing pixel, among the portion of the plurality of sensing pixels may correspond to different combinations of viewpoints.

Abstract: A method and apparatus for predicting an eye position based on an eye position measured in advance are provided. In the method and apparatus, predicted eye position data may be calculated using a plurality of predictors, and one or more target predictors may be determined among the plurality of predictors based on error information of each of the plurality of predictors. Final predicted eye position data may be acquired based on predicted eye position data calculated by the determined one or more target predictors.

Abstract: An operating method of a display apparatus includes calculating a range of a movement of a user based on eye movement information indicating movements of eyes of the user; and adjusting a stereoscopic depth of a three-dimensional (3D) image based on the range of the movement.

Abstract: There is provided a method and apparatus for tracking eyes of a user. The method and apparatus may acquire an image of the user, acquire an illuminance of a viewpoint from which the image is captured, and output coordinates of the eyes tracked from the image by operating at least one of a high illuminance eye tracker that operates at a high illuminance or a low illuminance eye tracker that operates at a low illuminance based on the acquired illuminance.

Abstract: An image processing apparatus includes a calculator configured to calculate a respective position offset for each of a plurality of candidate areas in a second frame based on a position of a basis image in a first frame and a determiner configured to determine a final selected area that includes a target in the second frame based on a respective weight allocated to each of the plurality of candidate areas and the calculated respective position offset.

Abstract: A method of determining eye position information includes identifying an eye area in a facial image; verifying a two-dimensional (2D) feature in the eye area; and performing a determination operation including, determining a three-dimensional (3D) target model based on the 2D feature; and determining 3D position information based on the 3D target model.

Abstract: An object tracking method and apparatus based on object verification are provided. The object tracking method includes detecting a first area that corresponds to a target object in a current frame, aligning the target object with respect to the current frame, and tracking the target object in a next frame based on whether a second area corresponds to the target object as a result of the alignment.

Abstract: A method and apparatus for eye tracking are disclosed. The method may include obtaining feature points corresponding to at least one portion of a face area of the user in an image, determining an inner area of an eye area of a first eye of the user based on the feature points, determining a pupil area of the user based on a pixel value of at least one pixel of the inner area, and determining an eye position of the user based on a position value of each pixel of the pupil area.

Abstract: Apparatuses and methods for processing image are provided. The apparatus includes a processor that generates a target region, including the subject, for each of a first frame image and a second frame image, among the plurality of frame images, extracts first feature points in the target region of the first frame image and second feature points in the target region of the second frame image, calculate disparity information by matching the first feature points extracted from the first frame image and the second feature points extracted from the second frame image and determine a distance between the subject and the image capturing device based on the calculated disparity.

Abstract: A computing apparatus for providing a three-dimensional (3D) interactive user experience (UX) is provided. The computing apparatus may include an object position estimator configured to calculate first coordinates recognized by a user as a position of a first point of an object in a stereoscopic image. The computing apparatus may include a pointing determiner configured to determine whether the user points to the first point based on the first coordinates and second coordinates, the second coordinates representing a pointing position of the user.

Abstract: A feature point positioning apparatus includes a memory storing computer-executable instructions; and one or more processors configured to execute the computer-executable instructions such that the one or more processors are configured to, iteratively update a first form coefficient based on, a nonlinear feature extracted from an image, and a regression factor matrix obtained through training, and detect a position of the feature point of the image based on, the updated first form coefficient, and a statistical form model obtained through training.

Abstract: Provided is an apparatus and method for predicting an eye position of user that may detect eye position coordinates in an image which is recently generated, determine a current prediction velocity of an eye movement based on a velocity of a previous eye movement, and predict the eye position coordinates of the user based on the detected eye position coordinates and the current prediction velocity.

Abstract: An object aligning method may include aligning an object in an input image corresponding to a quality of the input image.

Abstract: A method and apparatus for eye tracking are disclosed. The method may include obtaining feature points corresponding to at least one portion of a face area of the user in an image, determining an inner area of an eye area of a first eye of the user based on the feature points, determining a pupil area of the user based on a pixel value of at least one pixel of the inner area, and determining an eye position of the user based on a position value of each pixel of the pupil area.

Abstract: A classifier training method includes detecting error data from training data; and training a classifier configured to detect an object based on the error data.

Abstract: A method of determining eye position information includes identifying an eye area in a facial image; verifying a two-dimensional (2D) feature in the eye area; and performing a determination operation including, determining a three-dimensional (3D) target model based on the 2D feature; and determining 3D position information based on the 3D target model.

Abstract: An apparatus and method for predicting an eye position includes a storer configured to store detected position information of an eye of user during a sample time interval, a calculator configured to calculate a weighted average value of a variation of the detected position information, and a predictor configured to generate prediction position information of the eye of user at a target time based on the weighted average value, and the calculator is configured to apply a weight to the variation of the detected position information such that the weight increases as the target time is approached during the sample time interval.

Abstract: An apparatus for recognizing a pupillary distance for three-dimensional (3D) display includes a display configured to output a 3D image corresponding to a reference pupillary distance, a controller configured to control a viewing cone included in the 3D image, and a user inputter configured to receive a user feedback indicating whether an artifact is viewed in the 3D image in response to the controlling of the viewing cone. The controller may move the viewing cone within a margin corresponding to the reference pupillary distance, and change the reference pupillary distance or determine the reference pupillary distance to be a desired pupillary distance of the user based on the user feedback.

Abstract: A method of modeling a structure of a coronary artery of a subject may include: forming a learning-based shape model of the structure of the artery, based on positions of landmarks acquired from three-dimensional images; receiving a target image; and/or modeling the artery structure included in the target image, using the model. An apparatus for modeling a structure of a coronary artery may include: a memory configured to store a learning-based shape model of the artery, the learning-based shape model being formed based on positions of a plurality of landmarks acquired from three-dimensional images, the plurality of the landmarks corresponding to the artery; a communication circuit configured to receive a target image; and/or a processing circuit configured to model the artery structure included in the target image, using the model.