Embodiments shifts the color fields of a rendered image frame based on the eye tracking data (e.g. position of the user's pupils). An MR device obtains a first image frame having a set of color fields. The first image frame corresponds to a first position of the pupils of the viewer. The MR device then determines a second position of the pupils of the viewer based on, for example, data receive from an eye tracking device coupled to the MR device. The MR device generates, based on the first image frame, a second image frame corresponding to the second position of the pupils. The second image frame is generated by shifting color fields by a shift value based on the second position of the pupils of the viewer. The MR device transmits the second image frame to a display device of the MR device to be displayed thereon.

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.

Provided is a method of manufacturing an electronic apparatus, the method including approximating a surface of a three-dimensional (3D) structure with two-dimensional (2D) meshes, forming a developed view by developing the 2D meshes, manufacturing an electronic apparatus having the same shape as a shape of the developed view, and attaching the electronic apparatus to the surface of the 3D structure.

Anisotropic texture filtering applies a texture at a sampling point in screen space. Calculated texture-filter parameters configure a filter to perform filtering of the texture for the sampling point. The texture for the sampling point is filtered using a filtering kernel having a footprint in texture space determined by the texture-filter parameters. Texture-filter parameters are calculated by generating a first and a second pair of screen-space basis vectors being rotated relative to each other. First and second pairs of texture-space basis vectors are calculated that correspond to the first and second pairs of screen-space basis vectors transformed to texture space under a local approximation of a mapping between screen space and texture space. An angular displacement is determined between a selected pair of the first and second pairs of screen-space basis vectors and screen-space principal axes of a local approximation of the mapping that indicate the maximum and minimum scale factors of the mapping. The angular displacement and the pair of screen-space basis vectors are used to generate texture-space principal axes comprising a major axis associated with the maximum scale factor of the mapping and a minor axis associated with the minimum scale factor of the mapping.

Systems and methods relate to processing optical tomography coherence (OCT) images to predict characteristics of a treatment to be administered to effectively treat age-related macular degeneration. The processing can include pre-processing the image by flattening and/or cropping the image and processing the pre-processed image using a neural network. The neural network can include a deep convolutional neural network. An output of the neural network can indicate a predicted frequency and/or interval at which a treatment (e.g., anti-vascular endothelial growth factor therapy) is to be administered so as to prevent leakage of vasculature in the eye.

A method and apparatus for generating a face-harmonized image are disclosed. The method of generating a face-harmonized image includes receiving an input image, extracting facial landmarks from a target image and the input image, generating a face-removed image of the target image based on a facial mask region, extracting a user face image from the input image, transforming the user face image to correspond to the facial mask region, generating a face-blended image by blending the transformed user face image with the target image, extracting a feature map of the face-blended image, generating a combined feature map based on the feature map of the face-blended image and a feature map of the target image, generating a face harmonization result image based on the combined feature map, and providing the generated face harmonization result image.

A system for determining volume of a selectable region is configurable to (i) obtain user input directed to a 3D representation of a set of 2D images and (ii) based on the user input, selectively modify one or more mask pixels of one or more respective selection masks. Each 2D image of the set of 2D images is associated with a respective selection mask. The 3D representation represents pixels of the set of 2D images with corresponding voxels. The user input selects one or more voxels of the 3D representation. The one or more mask pixels is associated with one or more pixels of the set of 2D images that correspond to the one or more voxels of the 3D representation selected via the user input.

An image processing apparatus includes at least one processor, and the processor derives three-dimensional coordinate information that defines a position of a structure in a tomographic plane from a tomographic image including the structure, and that defines a position of an end part of the structure outside the tomographic plane in a direction intersecting the tomographic image.

Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.

The present invention relates to an image warping system capable of quickly performing image warping with low costs by using a cache memory, and a method thereof. The image warping system is provided to generate a transformed image by warping an input image with the help of a cache based WARP engine. The WARP engine accesses the input image and loads a portion of the image to the cache memory for speeding up the engine process. The WARP engine performs interpolation on the input image to generate an output image which is devoid of distortions. The output image obtained is then stored in the DDR of an electronic device.