Abstract: Systems and methods are provided for quantifying uncertainty of segmentation mask predictions made by machine learning models, where the uncertainty may be used to streamline an anatomical measurement workflow by automatically identifying less certain caliper placements. In one example, the current disclosure teaches receiving an image including a region of interest, determining a segmentation mask for the region of interest using a trained machine learning model, placing a caliper at a position within the image based on the segmentation mask, determining an uncertainty of the position of the caliper, and responding to the uncertainty of the position of the caliper being greater than a pre-determined threshold by displaying a visual indication of the position of the caliper via a display device and prompting a user to confirm or edit the position of the caliper.

Abstract: An electronic apparatus is disclosed. The electronic apparatus includes: a memory storing an input image, and a processor configured to: apply a filter to the input image to identify the input image as a plurality of areas, apply a first low-frequency filter to a first area among the plurality of areas, and apply a second low-frequency filter to a second area among the plurality of areas to perform downscaling, wherein a cut-off frequency of the second low-frequency filter is configured to be higher than a cut-off frequency of the first low-frequency filter.

Abstract: Disclosed are a signal processing device and an image display apparatus including the same. The signal processing device and the image display apparatus including the same according to an embodiment of the present disclosure includes: a first decoder to reconstruct image data received from an external electronic device, an encoder to compress the image data reconstructed in the first decoder, a memory to store the image data compressed in the encoder, and a second decoder to reconstruct the image data stored in the memory. Accordingly, despite of the increases of the amount of the input image data and the bandwidth thereof, the image data may be stored in the memory efficiently.

Abstract: A preprocessing device includes processing circuitry configured to acquire coordinate information of respective vertexes of a polygon for inside/outside determination and the coordinate information of the respective vertexes of the polygon for inside/outside determination and rotate a coordinate to be determined and the polygon for inside/outside determination as preprocessing of determination processing of determining whether the coordinate to be determined exists inside or outside the polygon for inside/outside determination, and output coordinate information of respective vertexes of the rotated polygon for inside/outside determination and the rotated coordinate to be determined as rotated coordinate information.

Abstract: A method including rendering graphics for an application using graphics processing units (GPUs). Responsibility for rendering of geometry is divided between GPUs based on screen regions, each GPU having a corresponding division of the responsibility which is known. First pieces of geometry are rendered at the GPUs during a rendering phase of a previous image frame. Statistics are generated for the rendering of the previous image frame. Second pieces of geometry of a current image frame are assigned based on the statistics to the GPUs for geometry testing. Geometry testing at a current image frame on the second pieces of geometry is performed to generate information regarding each piece of geometry and its relation to each screen region, the geometry testing performed at each of the GPUs based on the assigning. The information generated for the second pieces of geometry is used when rendering the geometry at the GPUs.

Abstract: Systems and method directed to generating a stylized image are disclosed. In particular, the method includes, in a first data path, (a) applying first stylization to an input image and (b) applying enlargement to the stylized image from (a). The method also includes, in a second data path, (c) applying segmentation to the input image to identify a face region of the input image and generate a mask image, and (d) applying second stylization to an entirety of the input image and inpainting to the identified face region of the stylized image. Machine-assisted blending is performed based on (1) the stylized image after the enlargement from the first data path, (2) the inpainted image from the second data path, and (3) the mask image, in order to obtain a final stylized image.

Abstract: Systems, methods, and storage media for automatically sizing one or more digital assets in a display rendered on a computing device are disclosed. Exemplary implementations may: select examples of digital assets to be displayed; create a set of training data structures; each train data structure including an aspect ratio of each digital asset and the predetermined height or width corresponding to the aspect ratio for that digital asset; perform polynomial regression analysis on the set of training data to determine a best fitted trend line and a corresponding polynomial equation; and apply the polynomial equation to at least one specific digital asset data structure to be displayed to thereby automatically calculate a size of the at least one specific digital asset as displayed.

Abstract: An electronic device may include a lenticular display. The lenticular display may have a lenticular lens film formed over an array of pixels. The display may have a number of independently controllable viewing zones. Each viewing zone displays a respective two-dimensional image. Each eye of the viewer may receive a different one of the two-dimensional images, resulting in a perceived three-dimensional image. The electronic device may include display pipeline circuitry that generates and processes content to be displayed on the lenticular display. Content generating circuitry may initially generate content that includes a plurality of two-dimensional images, each two-dimensional image corresponding to a respective viewing zone. Pre-processing circuitry may subsequently anisotropically resize each two-dimensional image. Pixel mapping circuitry may then be used to map the resized two-dimensional images to the array of pixels in the lenticular display.

Abstract: A system and method for evaluating a fragrance product or object provided, wherein the system includes a virtual reality component configured to present a user with a product or object in a virtual reality environment; an optional biometric sensor component configured to obtain the user's biometric data; a wearable scent delivery component configured to deliver a scent to the user; and a digital controller component that synchronizes deliver of the scent with the user's interaction with the product or object in the virtual reality environment.

Abstract: Methods and hardware for cube mapping comprise receiving fragment coordinates for an input block of fragments and texture instructions for the fragments and then determining, based on gradients of the input block of fragments, whether a first mode of cube mapping or a second mode of cube mapping is to be used, wherein the first mode of cube mapping performs calculations at a first precision for a subset of the fragments and calculations for remaining fragments at a second, lower, precision and the second mode of cube mapping performs calculations for all fragments at the first precision. Cube mapping is then performed using the determined mode and the gradients, wherein if the second mode is used and more than half of the fragments in the input block are valid, the cube mapping is performed over two clock cycles.

Abstract: A system may include a computer readable medium and a processor communicatively coupled to the computer readable medium. The processor may be configured to: obtain a graphical image file, the graphical image file including an image, wherein the image includes at least one sequence of repeating pattern elements, each of the at least one sequence including the repeating pattern elements that are repeated along a linear direction; and convert the graphical image file to at least one file including hardware directives that when executed cause a recreation of the image of the graphical image file to be drawn, wherein a file size of the at least one file is smaller than the graphical image file.

Abstract: A method and a system for processing an image and transform it into a high resolution and high-definition image using a computationally efficient image transformation procedure is provided. The transformation of the image comprises first transforming the image, also referred to as an intensity image, into a layered distance field (DF) image comprising an ordered sequence of multiple layers. Each layer in the ordered sequence is associated with a DF procedure and a set of rules for mapping the DF values to intensity values of the respective layer. The result of applying the DF procedures to each location in the intensity image is a transformed intensity image, which is of high definition and high resolution. The application of the DF procedures is governed by a stopping criteria based on error values between the intensity image and a reconstructed intensity image.

Abstract: Methods and systems are disclosed for quantizing images using machine-learning. A plurality of input images are received from a sensor (e.g., a camera), wherein each input image includes a plurality of pixels. Utilizing an image-to-image machine-learning model, each pixel is assigned a new pixel color. Utilizing a mixer machine-learning model, each new pixel color is converted to one of a fixed number of colors to produce a plurality of quantized images, with each quantized image corresponding to one of the input images. A loss function is determined based on an alignment of each input image with its corresponding quantized image via a pre-trained reference machine-learning model. One or more parameters of the image-to-image machine-learning model and the mixer model are updated based on the loss function. The process repeats, with each iteration updating the parameters of the image-to-image machine-learning model and the mixer model, until convergence, resulting in trained models.

Abstract: One embodiment of a method for rendering one or more graphics images includes tracing one or more rays through a graphics scene; computing one or more surface normals associated with intersections of the one or more rays with one or more surfaces, where computing each surface normal includes: computing a plurality of intermediate surface normals associated with a plurality of adjacent voxels of a grid, and interpolating the plurality of intermediate surface normals; and rendering one or more graphics images based on the one or more surface normals.

Abstract: A vehicle generates a city-scale map. The vehicle includes one or more Lidar sensors configured to obtain point clouds at different positions, orientations, and times, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform registering, in pairs, a subset of the point clouds based on respective surface normals of each of the point clouds; determining loop closures based on the registered subset of point clouds; determining a position and an orientation of each of the subset of the point clouds based on constraints associated with the determined loop closures; and generating a map based on the determined position and the orientation of each of the subset of the point clouds.

Abstract: A method of automatically providing personalized augmented mathematical learning content and activities to users of an online learning platform is described. Upon detecting that a user has captured an image or a video stream, one or more points of interest in the image or video stream are determined. Augmented mathematical information is then detected for the one or more points of interest and superimposed on the captured image or video stream.

Abstract: A system includes at least one imaging sensor and a processor. The processor is configured to acquire detected data describing an environment of an autonomous vehicle using the imaging sensor; derive reference data which describes the environment from a predefined map; compute difference data representing a difference between the detected data and the reference data; and transfer the difference data, wherein an image computed based on the difference data and the reference data represents the detected data. Other embodiments are also described.

Abstract: A virtual hair extension system is provided. The system includes a memory device having a user image of a user having hair, a display device, and a computer that is operably coupled to the memory device and the display device. The computer has a hair segmentation module and a hair extension blending module. The hair segmentation module generates a binary hair mask based on the user image. The hair extension blending module generates a final modified user image having the hair of the user with a selected hair extension thereon utilizing the user image, the binary hair mask, and a reference image of the selected hair extension. The computer displays the final modified user image having the hair of the user with the selected hair extension thereon on the display device.

Abstract: A computer implemented method rasterizes point cloud data. A number of processor units rasterizes the point cloud data into rasterized layers based on classes in which each rasterized layer in the rasterized layers corresponds to a class in the classes. The number of processor units creates key value pairs from the rasterized layers. The number of processor units store the key value pairs in a key value store. According to other illustrative embodiments, a computer system and a computer program product for rasterizing point cloud data are provided.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for generating object-specific-preset edits to be later applied to other digital images depicting a same object type or applying a previously generated object-specific-preset edit to an object of the same object type within a target digital image. For example, in some cases, the disclosed systems generate an object-specific-preset edit by determining a region of a particular localized edit in an edited digital image, identifying an edited object corresponding to the localized edit, and storing in a digital-image-editing document an object tag for the edited object and instructions for the localized edit. In certain implementations, the disclosed systems further apply such an object-specific-preset edit to a target object in a target digital image by determining transformed-positioning parameters for a localized edit from the object-specific-preset edit to the target object.