Abstract:
A plane detection apparatus for detecting at least one plane model from an input depth image. The plane detection apparatus may include an image divider to divide the input depth image into a plurality of patches, a plane model estimator to calculate one or more plane models with respect to the plurality of patches including a first patch and a second patch, and a patch merger to iteratively merge patches having a plane model a similarity greater than or equal to a first threshold by comparing plane models of the plurality of patches. When a patch having the plane model similarity greater than or equal to the first threshold is absent, the plane detection apparatus may determine at least one final plane model with respect to the input depth image using previously merged patches.
Abstract:
A method and apparatus for generating a depth image are provided. The apparatus receives an input image, extracts a feature corresponding to the input image, generates features for each depth resolution by decoding the feature using decoders corresponding to different depth resolutions, estimates probability distributions for each depth resolution by progressively refining the features for each depth resolution, and generates a target depth image corresponding to the input image based on a final estimated probability distribution from among the probability distributions for each depth resolution.
Abstract:
A method and apparatus for estimating a resist image (RI) are disclosed. The method includes obtaining an aerial image (AI) and a first RI from a mask image (MI), obtaining a second RI from the AI, and obtaining a third RI based on the first RI and the second RI.
Abstract:
An image processing method includes receiving an input image and a guide image corresponding to the input image, extracting informative features from the input image and the guide image to enhance the input image, selectively obtaining a first feature for the input image from among the informative features, and processing the input image based on the first feature.
Abstract:
A method and an apparatus for recognizing an object are disclosed. The apparatus may extract a plurality of features from an input image using a single recognition model and recognize an object in the input image based on the extracted features. The single recognition model may include at least one compression layer configured to compress input information and at least one decompression layer configured to decompress the compressed information to determine the features.
Abstract:
At least one example embodiment discloses a facial recognition apparatus configured to obtain a two-dimensional (2D) input image including a face region of a user, detect a facial feature point from the 2D input image, adjust a pose of a stored three-dimensional (3D) facial model based on the detected facial feature point, generate a 2D projection image from the adjusted 3D facial model, perform facial recognition based on the face region in the 2D input image and a face region in the 2D projection image, and output a result of the facial recognition.
Abstract:
A method and apparatus for detecting a three-dimensional (3D) point cloud point of interest (POI), the apparatus comprising a 3D point cloud data acquirer to acquire 3D point cloud data, a shape descriptor to generate a shape description vector describing a shape of a surface in which a pixel point of a 3D point cloud and a neighboring point of the pixel point are located, and a POI extractor to extract a POI based on the shape description vector is disclosed.
Abstract:
A processor-implemented method including generating a first corrected result image of a first desired pattern image using a backward correction neural network provided an input based on the first desired pattern image, the backward correction neural network performing a backward correction of a first process, generating a first simulated result image using a forward simulation neural network based on the first corrected result image, the forward simulation neural network performing a forward simulation of a performance of the first process, and updating the first corrected result image so that an error between the first desired pattern image and the first simulated result image is reduced.
Abstract:
A user authentication method and a user authentication apparatus acquire an input image including a frontalized face of a user, calculate a confidence map including confidence values, for authenticating the user, corresponding to pixels with values maintained in a depth image of the frontalized face of the user among pixels included in the input image, extract a second feature vector from a second image generated based on the input image and the confidence map, acquire a first feature vector corresponding to an enrolled image, and perform authentication of the user based on a correlation between the first feature vector and the second feature vector.
Abstract:
A method with image processing includes: generating a first surface normal image comprising surface normal vectors corresponding to pixels of a first depth image; and applying the first depth image and the first surface normal image to a first neural network, and acquiring a second depth image by changing the first depth image using the first neural network. The first neural network generates the second depth image to have an improved quality compared to the first depth image, based on an embedding vector that comprises a feature of the first depth image and a feature of the first surface normal image.