In implementations of object boundary generation, a computing device implements a boundary system to receive a mask defining a contour of an object depicted in a digital image, the mask having a lower resolution than the digital image. The boundary system maps a curve to the contour of the object and extracts strips of pixels from the digital image which are normal to points of the curve. A sample of the digital image is generated using the extracted strips of pixels which is input to a machine learning model. The machine learning model outputs a representation of a boundary of the object by processing the sample of the digital image.

A method, apparatus, device, and storage medium for transforming a hairstyle are provided. The method may include: determining a face bounding box according to information on face key points of acquired face image; constructing grids according to the face bounding box; deforming, by using an acquired target hairstyle function, edge lines of at least a part of the constructed grids, which comprises the hairstyle, to obtain a deformed grid curve; determining a deformed hairstyle in the face image according to the deformed grid curve.

Systems and methods for user guided iterative frame and scene segmentation are disclosed herein. The systems and methods can rely on overtraining a segmentation network on a frame. A disclosed method includes selecting a frame from a scene and generating a frame segmentation using the frame and a segmentation network. The method also includes displaying the frame and frame segmentation overlain on the frame, receiving a correction input on the frame, and training the segmentation network using the correction input. The method includes overtraining the segmentation network for the scene by iterating the above steps on the same frame or a series of frames from the scene.

Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

An image generation method and a computing device using the method, includes creating an image database with a plurality of original images, and obtaining a plurality of first outline images of an object by detecting an outline of the object in each of the original images. Numerous first feature matrixes are obtained by calculating a feature matrix of each of the first outline images. A second feature matrix of a second outline image input by a user is calculated. A target feature matrix is selected from the plurality of first feature matrixes, the target feature matrix has a minimum difference as the second feature matrix. A target image corresponding to the target feature matrix is matched and displayed from the image database. The method and device allow detection of an object outline in an image input by users and the generation of an image with the detected outline.

An image processing device (3000) comprises an input unit (3020) and a presentation unit (3040). The input unit (3020) accepts an input of an operation for movement, on a captured image captured by a camera, of a first image which is superimposed on the captured image on the basis of a predetermined camera parameter indicating the position and attitude of the camera and which indicates a target object having a predetermined shape and a predetermined size set in a real space. The presentation unit (3040) presents the first image indicating the target object in a manner of view corresponding to a position on the captured image after the movement on the basis of the camera parameter.

An image processing apparatus comprises processing circuitry. The processing circuitry is configured to select a specified cross section from three dimensional (3D) image data. And the processing circuitry is configured to perform control so as to form a curve corresponding to a curve delineated on the specified cross section, from the specified cross section to a cross section within a specified range, so as to form curved ace on the 3D image data.

Systems and methods for user guided iterative frame and scene segmentation are disclosed herein. The systems and methods can rely on overtraining a segmentation network on a frame. A disclosed method includes selecting a frame from a scene and generating a frame segmentation using the frame and a segmentation network. The method also includes displaying the frame and frame segmentation overlaid on the frame, receiving a correction input on the frame, and training the segmentation network using the correction input. The method includes overtraining the segmentation network for the scene by iterating the above steps on the same frame or a series of frames from the scene.

Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing an edge prediction neural network and edge-guided colorization neural network to transform grayscale digital images into colorized digital images. In one or more embodiments, the disclosed systems apply a color edge prediction neural network to a grayscale image to generate a color edge map indicating predicted chrominance edges. The disclosed systems can present the color edge map to a user via a colorization graphical user interface and receive user color points and color edge modifications. The disclosed systems can apply a second neural network, an edge-guided colorization neural network, to the color edge map or a modified edge map, user color points, and the grayscale image to generate an edge-constrained colorized digital image.