Systems and methods for automatically grading a user device are provided. Such systems and methods can include (1) a lighting element positioned at an angle relative to a platform, (2) an imaging device positioned at the angle relative to the platform such that light emitted from the lighting element and a field of view of the imaging device form a right angle where the light emitted from the lighting element and the field of view meet at a user device when the user device is positioned at a predetermined location on the platform, and (3) control circuitry that can activate the lighting element, instruct the imaging device to capture an image of a screen of the user device while the user device is at the predetermined location and is being illuminated by the first lighting element, and parse the image to determine whether the screen is damaged.

Systems are configured for facilitating light leak correction for a display device. The systems are configured to obtain a color value for a pixel and obtain a light leak value associated with the pixel. The light leak value is based on a measured light leakage associated with a color filter of a display device configured to display the pixel. Systems are also configured to generate an updated color value for the pixel by applying a light leak compensation operation to the color value for the pixel. The light leak compensation operation uses the light leak value as an input for modifying the color value to compensate for light leakage that may otherwise occur when displaying the pixel on the display device. Systems are also configured to trigger display of the pixel on the display device using the updated color value.

The present disclosure provides a method for evaluating an image acquisition accuracy of a Demura device, including: controlling a display panel to display a detection picture, wherein the detection picture includes a plurality of test point patterns with an interval therebetween; acquiring an image of the detection picture by the Demura device to obtain a preprocessed image corresponding to the detection picture, wherein the preprocessed image and a corresponding detection picture have a same size and a same shape; and determining the image acquisition accuracy of the Demura device according to a difference between a position of each of the plurality of test point patterns in the detection picture and a corresponding position of the test point pattern in the preprocessed image. The present disclosure also provides an apparatus for evaluating an image acquisition accuracy of a Demura device, an electronic device and a non-transitory computer-readable storage medium.

A method and a system for inspecting a display image are provided. A test image is displayed on a display surface through a display device. An optical inspection image is generated by photographing the test image on the display surface by an image capturing device. A brightness channel image and a color channel image of the optical inspection image are obtained by performing color space transformation on the optical inspection image. Background estimation is applied to the color channel image to obtain a color channel background image. At least one color nonuniformity region in the test image is obtained by respectively comparing an inspection reference value with multiple color component pixel values of the color channel background image.

There is presented a system and method for detecting mobile device fault conditions, including detecting fault conditions by software operating on the mobile device. In one embodiment, the present invention provides for systems and methods for using a neural network to detect, from an image of the device, that the mobile device has a defect, for instance a cracked or scratched screen. Systems and methods also provide for, reporting the defect status of the device, working or not, so that appropriate action may be taken by a third party.

A method of detecting a stain of a display panel is disclosed that includes capturing a first color image of the display panel, capturing a second color image of the display panel, generating a merged image by merging a color coordinate map of the first color image and a color coordinate map of the second color image, generating a background image of the merged image by a morphology filtering, generating a flattened image by operating the merged image and the background image and detecting an area of the flattened image in which a color coordinate value exceeds a threshold value as a stain area.

A method of producing a tablet in which an uncoated tablet is coated by a coating agent, the method including: a coating process of coating uncoated tablets with a coating agent by spray coating the coating agent onto tablets that are churned and tumbled inside a container, and drying the tablets inside the container by supplying drying air into the container and exhausting air from the container, wherein spray coating conditions, including air supply temperature, air supply rate, and spray speed, are controlled according to the weight of the coating agent with which the uncoated tablets are coated, such that the humidity of air exhausted during spray coating is within a range of from 14% RH to 30% RH.

The present disclosure generally relates to machine vision systems, illumination sources for use in machine vision systems, and components for use in the illumination sources. More specifically, the present disclosure relates to machine vision systems incorporating multi-function illumination sources, multi-function illumination sources, and components for use in multi-function illumination sources.

An object of the present invention is to provide an information processing method and a computer program that can suppress an increase in inspection time in the manufacturing process of the monitors.

The present invention provides an information processing method comprising: an error calculation step of calculating an error between input image data input to an autoencoder and output image data output from the autoencoder; a similarity calculation step of calculating a similarity between compressed data and reference data based on the compressed data and the reference data, the compressed data being acquired by compressing the input image data in an encoder of the autoencoder; and a determination step of determining whether a display unevenness of the input image data is acceptable based on a relationship between the error and the similarity, the relationship corresponding to a relational expression or a table.

A method where deviations of a characteristic of an image simulated by two different process models or deviations of the characteristic simulated by a process model and measured by a metrology tool, are used for various purposes such as to reduce the calibration time, improve the accuracy of the model, and improve the overall manufacturing process.