This disclosure includes technologies for image processing, particularly for image generation and editing in a configurable semantic direction. A generative adversarial network is trained with an auxiliary network with an auxiliary task that is designed to disentangle the latent space of the generative adversarial network. Resultantly, a new type of GAN is created to improve image generation or editing in both conditional and unconditional settings.

An eye center localization method includes performing an image sketching step, a frontal face generating step, an eye center marking step and a geometric transforming step. The image sketching step is performed to drive a processing unit to sketch a face image from the image. The frontal face generating step is performed to drive the processing unit to transform the face image into a frontal face image according to a frontal face generating model. The eye center marking step is performed to drive the processing unit to mark a frontal eye center position information on the frontal face image. The geometric transforming step is performed to drive the processing unit to calculate two rotating variables between the face image and the frontal face image, and calculate the eye center position information according to the two rotating variables and the frontal eye center position information.

A deep-learning-based method for metal artifact reduction in CT images includes providing a dataset and a cGAN. The dataset includes CT image pairs, randomly partitioned into a training set, a validation set, and a testing set. Each Pre-CT and Post-CT image pairs is respectively acquired in a region before and after an implant is implanted. The Pre-CT and Post-CT images of each pair are artifact-free CT and artifact-affected CT images, respectively. The cGAN is conditioned on the Post-CT images, includes a generator and a discriminator that operably compete with each other, and is characterized with a training objective that is a sum of an adversarial loss and a reconstruction loss. The method also includes training the cGAN with the dataset; inputting the post-operatively acquired CT image to the trained cGAN; and generating an artifact-corrected image by the trained cGAN, where metal artifacts are removed in the artifact-corrected image.

The present disclosure discloses a method and device of dynamic processing of an image and a computer-readable storage medium. Based on the position data of the critical points in the original image and the target image, by unit splitting and affine transformation, the mapping relation between any two neighboring states of the initial state, the intermediate states and the ending state is determined, in turn the intermediate images formed in the intermediate states are determined and obtained based on the mapping relation and the correspondence of all of the points in the basic units, and finally the original image, the intermediate images and the target image are sequentially displayed to present a dynamic effect of the images.

Example-based affine registration is provided. In various embodiments, a plurality of training images is read. A predetermined affine transform is read for each of the plurality of training images. Each affine transform maps its associated image to a template. Weights are determined for each of the plurality of training images. The weights are determined to minimize a difference between the test image and a weighted linear combination of the training images. An affine transform is determined mapping the test image to the template by computing a weighted linear combination of the affine transforms using the weights.

A computer device obtains a to-be-annotated image having a first magnification. The device obtains an annotated image from an annotated image set, the annotated image distinct from the to-be-annotated image and having a second magnification that is distinct from with the first magnification. The annotated image set includes at least one annotated image. The device matches the to-be-annotated image with the annotated image to obtain an affine transformation matrix, and generates annotation information of the to-be-annotated image according to the affine transformation matrix and the annotated image. In this way, annotations corresponding to images at different magnifications may be migrated. For example, the annotations may be migrated from the low-magnification images to the high-magnification images, thereby reducing the manual annotation amount and avoiding repeated annotations, and further improving annotation efficiency and reducing labor costs.

A meme generation method, an electronic device, and a storage medium are provided. The method includes: determining a plurality of second expression images corresponding to a target face image based on a plurality of first expression images contained in a first meme; generating a second meme corresponding to the target face image based on the plurality of second expression images corresponding to the target face image; wherein, determining an affine transformation parameter between the target face image and an i-th first expression image in the plurality of first expression images according to a corresponding relation between a face key point in the target face image and a face key point in the i-th first expression image; and transforming the target face image based on the affine transformation parameter to obtain an i-th second expression image corresponding to the target face image.

Disclosed herein, inter alia, are methods and systems of image analysis useful for identifying and/or quantifying features in patterns.

A method is provided. The method includes: obtaining a picture to be processed, where the picture to be processed includes a plurality of pixels, and the plurality of pixels comprise first pixels for forming an image and second pixels for forming an image background; rotating the picture to be processed, where for each rotation angle, an intermediate picture is obtained; selecting at least two pictures from the picture to be processed and several intermediate pictures for calculating an area of a bounding box surrounding the image respectively; and removing second pixels outside the bounding box in a picture with the smallest area of bounding box to obtain a processed picture.

An apparatus for generating a map identifies a lane line and a feature other than a lane line on a road from a first image of a predetermined location of the road taken downward from the sky; identifies the lane line and the feature from a second image representing the predetermined location of the road and made based on images taken by a camera provided for a vehicle; aligning the first images with the second images, based on the predetermined location; deforms the second image so that the feature in the second image best fits the feature in the first image; further deforms the second image in a direction perpendicular to the front-back direction of the road so that the position of the feature remains unchanged and that the positions of the lane lines in the first and second images match; and combines the first and deformed second images.