An animated static display, animated static display system, and method provide a plurality of static images. The animated static display includes a plurality of directional scattering elements arranged across a light guide and configured to scatter out light provided from different light sources and guided by the light guide as directional light beams having different directions corresponding to the different light sources. The animated static display also includes a barrier layer having different sets of apertures configured to pass directional light beams having the different directions to provide corresponding different static images of the static image plurality. The animated static display system further includes a mode controller configured to selectively activate the different light sources to provide an animated image comprising the different static images.

Provided are an image processing method and device, a mobile terminal, and a storage medium. The method includes: acquiring a first image output by an image sensor; responsive to that a first signal value of the first image after being processed exceeds a saturation value of a first bit width at a preset image processing stage, recording the first signal value of the processed first image as a second signal value with a second bit width, the second bit width being greater than the first bit width; and mapping the second signal value recorded with the second bit width to a third signal value recorded with the first bit width to obtain a second image. Through the method, an operation of increasing a bit width can be used to retain more information of an image and improve the quality of the image, and an implementation is simple and effective.

Methods and apparatus are disclosed for implementing data augmentation within a storage controller of a data storage device based on machine learning data read from a non-volatile memory (NVM) array of a memory die. Some particular aspects relate to configuring the storage controller to generate augmented versions of training images for use in training a Deep Learning Accelerator of an image recognition system by rotating, translating, skewing, cropping, etc., a set of initial training images obtained from a host device and stored in the NVM array. Other aspects relate to controlling components of the memory die to generate noise-augmented images by, for example, storing and then reading training images from worn regions of the NVM array to inject noise into the images. Data augmentation based on data read from multiple memory dies is also described, such as image data spread across multiple NVM arrays or multiple memory dies.

An image processing apparatus comprises a processing unit configured to provide an image containing a first object and a second object, with a lighting effect from a virtual light source, a setting unit configured to set a parameter of the virtual light source; and a designating unit configured to designate the first object and/or the second object, based on a user's operation, wherein, in a case in which the designating unit has designated at least one of the first object and the second object, the first object and the second object are provided with an effect of virtual light from a same direction, and in a case in which the designating means has not designated any object, the first object and the second object are provided with an effect of virtual light from different directions.

A citrus identification method comprises: performing first-time image acquisition processing on a target citrus tree to obtain a first image; inputting the first image into a first citrus fruit identification model to be processed to obtain a first identification result sequence; performing area interception processing on the first image to obtain a citrus fruit area; obtaining roundness integrity numerical values; selecting an appointed roundness integrity numerical value, and acquiring a defect position of an appointed citrus fruit in the first image; determining a first spatial range and a second spatial range; performing both first spray injection treatment and second spray injection treatment; performing second-time image acquisition processing to obtain a second image; inputting the second image into a second citrus fruit identification model to be processed to obtain a second identification result; and generating a citrus fruit identification result.

Provided are a fisheye camera calibration system, method and an electronic device. The system includes a hemispherical target, a fisheye camera and an electronic device. The hemispherical target includes a hemispherical inner surface and multiple markers provided on the hemispherical inner surface. The fisheye camera is used for photographing the hemispherical target and acquiring a target image, where the hemispherical target and the multiple markers provided on the hemispherical inner surface are captured in the target image. The electronic device is used for acquiring initial values of k.sub.1, k.sub.2, k.sub.3, k.sub.4, k.sub.5, u.sub.0, v.sub.0, m.sub.u and m.sub.v, and using a Levenberg-Marquardt algorithm to optimize the initial values of k.sub.1, k.sub.2, k.sub.3, k.sub.4, k.sub.5, u.sub.0, v.sub.0, m.sub.u and m.sub.v, so as to determine imaging model parameters of the fisheye camera.

Introduced here are diagnostic platforms able to produce visualizations that visually highlight the pixels considered as evidence of a medical condition by a computer-aided diagnostic (CADx) model. A CADx model may be based on, for example, a multi-headed neural network designed to detect the presence/progression of multiple medical conditions. By explaining how each output is produced by the multi-headed neural network, a diagnostic platform can identify the latent variable (s) responsible for producing each output. For example, a diagnostic platform may create a multi-variable heatmap that visually distinguishes the pixels considered as evidence of each multiple condition by a multi-headed neural network. To accomplish this, the diagnostic platform may create multiple heatmaps by producing, for each output, a separate heatmap that distinguishes the pixels in the image considered as evidence of the corresponding medical condition by the multi-headed neural network, and then compiling the multiple heatmaps into the multi-variable heatmap.

A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

A computer system for determining onset of an acute coronary syndrome (ACS) event in a remote computing environment comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories is provided. The stored program instructions include capturing, using a camera, a first image at a first time of an iris and a pupil of a first eye of a user; following the capturing of the first image, identifying in the first image a first iris information; capturing, using the camera, a second image at a second time of the iris and the pupil of the first eye of the user; following the capturing of the second image, identifying in the second image a second iris information; determining whether the first iris information is within an allowable range of the second iris information; and providing an indication of a likely ACS event based on a determination of whether the first iris information is within the allowable range of the second iris information.

A unit plate position fixation method and device and a lamination machine are provided. The unit plate position fixation method includes: obtaining peripheral position information of unit plates located between two layers of separators; controlling a hot-pressing device according to the peripheral position information to perform hot pressing on the two layers of separators, so that the two layers of separators are adhered at peripheral positions of the unit plates.