Abstract:
A tomography apparatus includes a data acquirer configured to acquire partial images of an object including a first image and a second image having imaged a surface of a portion of the object corresponding to a first time and a second time, respectively, by performing a tomography scan on the object that is moving, and acquire first information indicating motion of the object by using the first image and the second image; and an image reconstructor configured to reconstruct a target image by using the first information.
Abstract:
A method and apparatus with depth map generation. The method may include generating points for a point cloud by unprojecting multi-view depth maps, of plural views, into a corresponding three-dimensional (3D) space using respective camera parameters corresponding to each view of the multi-view depth maps, extracting feature embedding vectors corresponding to the generated points, generating a two-dimensional (2D) feature map of a set view based on the extracted feature embedding vectors, generating a residual depth map using a refinement network with respect to the 2D feature map, generating a new depth map based on the residual depth map and an initial depth map, of the set view, among the multi-view depth maps.
Abstract:
A method with algorithm updating includes: receiving a first input batch including one or more first images; generating a first output batch with respect to the first input batch using an algorithm configured to generate a disparity image, the first output batch including one or more first output images; receiving a second input batch corresponding to the first input batch, the second input batch including one or more second images having viewpoints that are different from viewpoints of the one or more first images; generating a test batch based on the first output batch and the second input batch, the test batch including one or more test images; and updating the algorithm based on a difference between the first input batch and the test batch.
Abstract:
A tomography apparatus includes a data acquirer acquiring a first image and a second image that are partial images, by using data acquired in a first angular section corresponding to a first time point and a second angular section corresponding to a second time and facing the first angular section, by performing a tomography scan on an object that is moving, and acquiring first information indicating a motion amount of the object by using the first image and the second image, and an image reconstructor reconstructing a target image indicating the object at a target time, based on the first information.
Abstract:
A tomography apparatus includes a data acquirer which acquires a first image which corresponds to a first time point and a second image which corresponds to a second time point by performing a tomography scan on an object; an image reconstructor which acquires first information which relates to a relationship between a motion amount of the object and the time based on a motion amount between the first image and the second image, predicts a third image which corresponds to a third time point between the first and second time points based on the first information, corrects the first information by using the predicted third image and measured data which corresponds to the third time point, and reconstructs the third image by using the corrected first information; and a display which displays the reconstructed third image.
Abstract:
Disclosed are an apparatus and method with video processing. A computing apparatus includes one or more processors and storage storing instructions configured to, when executed by the one or more processors, cause the one or more processors to: generate cluster maps using clusters generated by performing clustering, wherein the generating the cluster maps performed based on a reference frame of a video generated based on a previous point in time of the video, generate a predicted frame by performing motion compensation based on the cluster maps, and generate a decoded frame by performing decoding based on the current frame and the predicted frame.
Abstract:
A method and apparatus with image depth estimation are provided. The method includes obtaining a first statistical value associated with a depth for each of plural pixels included in an input image based on a first channel of output data obtained by applying the input image to a neural network, obtaining a second statistical value associated with a depth for each of the plural pixels in the input image based on a second channel of the output data, and estimating depth information of each of the plural pixels in the input image based on the first statistical value and the second statistical value. The neural network may be trained based on a probability distribution for a depth of each pixel in an image based on a first statistical value and a second statistical value that are obtained corresponding to an image with predetermined depth information in the training data.
Abstract:
A medical imaging apparatus includes an X-ray source configured to irradiate X-rays to an object; an X-ray detector configured to detect the X-rays radiated from the X-ray source to obtain projection data; and an image processor configured to reconstruct the projection data based on a motion parameter representing movement of at least one of the object, the X-ray source, and the X-ray detector, and to generate a medical image by applying a weighting process to the reconstructed projection data.
Abstract:
Provided is a medical image processing apparatus including a processor. The processor obtains raw data in a first phase section and generates first motion information by using at least one partial angle reconstruction (PAR) image pair including two PAR images respectively obtained in two phase sections in the first phase section that face each other. The processor also generates a summed image by summing a plurality of PAR images obtained at different phases within the first phase section by using the first motion information Second motion information is generated by updating the first motion information such that an image metric representing motion artifacts is minimized when being calculated from the summed image and a reconstructed image is generated by applying the second motion information to the raw data.