Abstract:
A fingerprint verification method and apparatus is disclosed. The fingerprint verification method may include obtaining an input fingerprint image, determining a matching region between the input fingerprint image and a registered fingerprint image, determining a similarity corresponding to the matching region, representing a determined indication of similarities between the input fingerprint image and the registered fingerprint image, relating the determined similarity to the matching region as represented in a matching region-based similarity, determining a result of a verification of the input fingerprint image based on the matching region-based similarity, and indicating the result of the verification.
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 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 with depth image generation may include: receiving an input image; generating a first low-resolution image having a resolution lower than a resolution of the input image; acquiring a first depth residual image corresponding to the input image by using a first generation model based on a first neural network; generating a first low-resolution depth image corresponding to the first low-resolution image by using a second generation model based on a second neural network; and generating a target depth image corresponding to the input image, based on the first depth residual image and the first low-resolution depth image.
Abstract:
A method of adaptively updating an enrollment database is disclosed. The method may include extracting a first feature vector from an input image, the input image including a face of a user, determining whether to enroll the input image in the enrollment database based on the first feature vector, second feature vectors of enrollment images and a representative vector, the second feature vectors of the enrollment images being enrolled in the enrollment database, and the representative vector representing the second feature vectors, and enrolling the input image in the enrollment database based on a result of the determining.
Abstract:
A method of generating a three-dimensional (3D) face model includes extracting feature points of a face from input images comprising a first face image and a second face image; deforming a generic 3D face model to a personalized 3D face model based on the feature points; projecting the personalized 3D face model to each of the first face image and the second face image; and refining the personalized 3D face model based on a difference in texture patterns between the first face image to which the personalized 3D face model is projected and the second face image to which the personalized 3D face model is projected.
Abstract:
A three-dimensional (3D) face modeling method and apparatus is disclosed. The 3D face modeling apparatus may generate a personalized 3D face model using a two-dimensional (2D) input image and a generic 3D face model, obtain a depth image and a texture image using the generated personalized 3D face model, determine a patch region of each of the depth image and the texture image, and adjust a shape of the personalized 3D face model based on a matching relationship between the patch region of the depth image and the patch region of the texture image.
Abstract:
An apparatus and corresponding method are provided to match images and include assigning depth candidate values to a pixel in a first image, and reassigning third depth candidate values to a first pixel in the first image based on first depth candidate values assigned to the first pixel and second depth candidate values assigned to a second pixel adjacent to the first pixel. The apparatus and method also include determining one of the third depth candidate values to be a depth value of the first pixel, and matching the first pixel and a third pixel in a second image corresponding to the determined depth value of the first pixel.
Abstract:
An apparatus for estimating a camera pose includes an image acquisition unit to acquire a photographed image, a motion sensor to acquire motion information of the apparatus for estimating the camera pose, a static area detector to detect a static area of the photographed image based on the photographed image and the motion information, and a pose estimator to estimate a camera pose based on the detected static area.
Abstract:
Provided are an electronic device including a transparent display and a method of controlling the electronic device, the method including acquiring a first image based on an optical display mode in which light incident from an object is transmitted; acquiring a second image based on a video display mode in which an image captured from the object is displayed; and displaying the first image and the second image on the transparent display by mixing the first image and the second image.