Embodiments of this application disclose a method for determining camera pose information of a camera of a mobile terminal. The method includes: obtaining a first image, a second image, and a template image, the first image being a previous frame of image of the second image, the first image and the second image being images including a respective instance of the template image captured by the mobile terminal using the camera at a corresponding spatial position; determining a first homography between the template image and the second image; determining a second homography between the first image and the second image; and performing complementary filtering processing on the first homography and the second homography, to obtain camera pose information of the camera, wherein the camera pose information of the camera represents a spatial position of the mobile terminal when the mobile terminal captures the second image using the camera.

One embodiment of a method includes applying a first generator model to a semantic representation of an image to generate an affine transformation, where the affine transformation represents a bounding box associated with at least one region within the image. The method further includes applying a second generator model to the affine transformation and the semantic representation to generate a shape of an object. The method further includes inserting the object into the image based on the bounding box and the shape.

Techniques are described for dynamically correcting image distortion during imaging of a patterned sample having repeating spots. Different sets of image distortion correction coefficients may be calculated for different regions of a sample during a first imaging cycle of a multicycle imaging run and subsequently applied in real time to image data generated during subsequent cycles. In one implementation, image distortion correction coefficients may be calculated for an image of a patterned sample having repeated spots by: estimating an affine transform of the image; sharpening the image; and iteratively searching for an optimal set of distortion correction coefficients for the sharpened image, where iteratively searching for the optimal set of distortion correction coefficients for the sharpened image includes calculating a mean chastity for spot locations in the image, and where the estimated affine transform is applied during each iteration of the search.

Disclosed are an image processing method performed by an electronic device. After a target image for motion transfer and at least one source image corresponding to the target image are acquired, multi-dimensional feature extraction is performed on the source and target images to acquire keypoint feature information of corresponding keypoints in the source and target images, and appearance feature information corresponding to the source image, and the keypoint feature information includes keypoint perspective information. Then, perspective transformation is performed on the keypoints according to the keypoint perspective information to acquire optic flow information of the keypoints. Motion information corresponding to the keypoints is then determined based on the optic flow information and the keypoint feature information, and the motion information and the appearance feature information are fused to acquire a processed image of an object in the source image after transferring motion of an object in the target image.

Generation of three-dimensional (3D) object models may be challenging for users without a sufficient skill set for content creation and may also be resource intensive. One or more style transfer networks may be used for part-aware style transformation of both geometric features and textural components of a source asset to a target asset. The source asset may be segmented into particular parts and then ellipsoid approximations may be warped according to correspondence of the particular parts to the target assets. Moreover, a texture associated with the target asset may be used to warp or adjust a source texture, where the new texture can be applied to the warped parts.

A radio frequency (RF) imaging device comprising a display receives a three-dimensional (3D) image that is a superposition of two or more images having different image types, which may include at least a 3D RF image of a space disposed behind a surface. A plurality of input control devices receive a user input for manipulating the display of the 3D image. Alternatively or additionally, the radio frequency (RF) imaging device may receive a three-dimensional (3D) image that is a weighted combination of a plurality of images, which may include a 3D RF image of a space disposed behind a surface, an infrared (IR) image of the surface, and a visible light image of the surface. A user input may specify changes to the weighted combination. In another embodiment, the RF imaging device may include an output device that produces a physical output indicating a detected type of material of an object in the space.

A method detects mechanical equipment parts. The method includes: obtaining an image of a part; extracting a feature from the image using a machine learning model, identifying a type of surface defect on the basis of the feature to obtain an identification result; and determining whether to replace the part on the basis of the identification result and a predetermined standard of the part. The method reduces the difficulty of detecting a part, can accurately identify a surface defect of the part and determine whether the part needs to be replaced, thereby improving the work efficiency, and shortens the time for mechanical equipment to stop operating for maintenance, thus improving the operating efficiency of the mechanical equipment. The method is automatically executed by a computer, thereby avoiding manually checking errors, improving the accuracy of detection results, and thus improving the reliability of operation of the mechanical equipment.

Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.

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.

A method and apparatus to automatically calibrate one or more attributes of a gaming system. For instance, the gaming system determines, in response to analysis by a processor of image data via a machine-learning model, an orientation of an affixed (e.g., printed) fiducial marker positioned in a known location on a planar playing surface of a gaming table. The system also transforms, in response to determining the orientation, first geometric data associated with an object on the planar playing surface to isomorphically equivalent second geometric data. The system also digitally illustrates, via an augmented reality overlay of the image data using the isomorphically equivalent second geometric data, a graphical representation of the object positioned relative to the fiducial marker on the planar playing surface.