A mask inspection method and a mask inspection apparatus for inspecting a deposition mask are provided. A mask inspection method includes: providing a deposition mask including a plurality of first openings and a plurality of second openings, each of the first openings having a shape in a plan view different from that of each of the second openings; designating a first group and a second group, each including at least one of the first openings and at least one of the second openings; comparing an image of the first group with an image of the second group; and determining whether the first group and the second group are defective, an arrangement of the first and second openings of the first group being the same as an arrangement of the first and second openings of the second group.

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 positioning method including: acquiring a gray-scale image of an object to be measured to determine a gray-scale value of each pixel on the gray-scale image; determining a target area to be measured and a first gray-scale change area adjacent to the target area to be measured based on the gray-scale value of each pixel; determining a target gray-scale value based on a gray-scale value of the target area to be measured and a gray-scale value of each pixel in the first gray-scale change area; determining target pixels that conform to the target gray-scale value in the first gray-scale change area, to use the target pixels as actual edge pixels of the target area to be measured; and determining a relative distance between a center position of the target area to be measured and a reference position, to obtain position information of the target area.

A system and a method for analyzing files using visual cues in the presentation of the file is provided. These visual aids may be extracted using a convolutional neural network, classified, and used in conjunction with file metadata to determine if a provided document is likely to be malicious. This methodology may be extended to detect a variety of social engineering-related attacks including phishing sites or malicious emails. A method for analyzing a received file to determine if the received file comprises malicious code begins with generating an image that would be displayed if the received file is opened by the native software program. Then the image is analyzed, and output is generated. Metadata is also extracted from the received file. Then, a maliciousness score is generated based on the output, the metadata, and a reference dataset.

A method for detecting defects in a near-eye display is provided. The method includes the following steps: obtaining a reference image and a DUT image according to a first image and a second image captured by a camera through a Fresnel lens when a display panel respectively displays a test-pattern image and a test-background image; performing a fast Fourier transform on the reference image and the DUT image to obtain a frequency-domain reference image and a frequency-domain DUT image; calculating an average value of pixel values above a predetermined cut-off ratio in a histogram of each first region of interest (ROI) in a filtered frequency-domain reference image as a corresponding threshold; comparing each pixel in the filtered DUT image with the corresponding threshold to generate a determination result; and building a defective-status map of the near-eye display according to the determination results.

The present disclosure relates to a method and a device for compensating for a luminance deviation, in which test pixel data is written to pixels disposed on a screen of a display panel and an image of the screen is captured, a luminance deviation is determined by analyzing luminance characteristics based on a measured luminance value of each of the pixels, which is obtained from data of the image of the screen captured by an imaging device, and a defective pixel is detected, and compensation data for reducing the luminance deviation between the pixels is derived for each pixel, so that a measured luminance value or compensation data of the defective pixel can be removed.

Disclosed are a method and a device for marking an image position of a sub-pixel of a display screen, and a storage medium. The method includes: obtaining an original image of the sub-pixel of the display screen that has been taken, marking all the sub-pixel bright points in the original image of the sub-pixel to obtain a sub-pixel mark map; searching for points to be filled among adjacent points of the bright point of each sub-pixel in the sub-pixel mark map according to the first direction pixel pitch and the second direction pixel pitch in the original image of the sub-pixel, filling the points to be filled to obtain a filling mark map; and obtaining the target sub-pixel mark map based on the sub-pixel mark map and the filling mark map.

The application discloses an automatic screen state detection robot, comprising a memory having an automatic screen state detection program stored thereon and a processor, and the automatic screen state detection program being executed by the processor to implement the operations of: controlling the robot to move into a preset area of each of service devices in a self-service branch respectively; detecting whether a service device has a circuit fault or not if the robot moves into the preset area of the service device; and controlling the service device to display an image according to preset display parameters if the display screen has no circuit fault, and analyzing the image displayed on the display screen to find whether the display screen of the service device has an abnormality of a preset type. The application also provides an automatic screen state detection method and a computer-readable storage medium.

Reciprocal space map of specific sample locations is generated based on the sample images acquired by irradiating the sample with a charged particle beam at multiple incident angles. The incident angles are obtained by tilting the charged particle beam and/or the sample around two perpendicular axes within the sample plane. The reciprocal space map of a selected sample location is generated based on intensity of pixels corresponding to the location in the sample images.

The present disclosure provides a detection method of a projector, a projector, and an electronic device. The method includes: obtaining a speckle pattern, the speckle pattern being an image generated by irradiating laser light generated by a light source in the projector onto an object by an optical element; and performing an abnormality detection on the projector according to the speckle pattern.