An image processing system includes one or more processors that select one target algorithm from evaluation algorithms, calculate an evaluation value indicating adaptation to image measurement using the target algorithm and one or more evaluation images corresponding to an evaluation lighting pattern for each evaluation lighting pattern, and determine a lighting pattern to be used for the image measurement from evaluation lighting patterns based on the evaluation value.

A belt inspection system to detect a belt defect of an intermediate transfer belt of an image forming apparatus using a first belt image obtained by photographing the intermediate transfer belt, performs a preprocessing on the first belt image to generate a second belt image, detects a candidate for the belt defect from the second belt image, and performs, in the preprocessing, a noise removal processing to remove a specific noise included in the first belt image, the specific noise including a noise caused by the photographing, a noise of a specific size based on an average size of the belt defect, and a band-shaped or streak-shaped noise extending in the width direction of the intermediate transfer belt.

A computer-implemented method for measuring the three-dimensional geometry of a mineral glass-based glazing during or just after a temperature shaping operation. The method implements digital processing and calculations, based on the principles of stereocorrelation, of digital images of a random light pattern projected onto the surface of a transparent glazing. It makes it possible in particular to determine the shape or the three-dimensional geometry of a transparent glazing, the surface of which not be totally opacified and/or which radiates under the effect of temperature.

A method for analysis of algae, comprising: receiving a microscopic image of algae by a cloud server (2501), the microscopic image including a scaling pattern for determining a magnification; determining the magnification by the cloud server based on the scaling pattern (2502); and analyzing the microscopic image by the cloud server based on the magnification to obtain an analysis result (2503).

The present disclosure relates to technology configured to enable fault detection and condition assessment of underground stormwater and sewer pipes. Embodiments of the present disclosure have been developed to allow automated processing of video captured by pipe inspection robots and the like thereby to identify and categorize artefacts in pipes.

The present invention provides an image processing method for CDSEM for determining a measuring range of an image of a target pattern measured by a CDSEM machine. The image processing method of CDSEM comprises: obtaining a first gray scale image based on the image of the target pattern; performing Fourier transform to the first gray scale image to obtain a first frequency spectrum distribution; filtering out frequency spectrum components whose absolute values of ordinate are greater than preset threshold in the first frequency spectrum distribution to obtain a second frequency spectrum distribution, the preset threshold relates to the background noise and the signal frequency of SRAF features; and determining the measuring range based on the second frequency spectrum distribution.

An inspection device includes control unit that acquires pre-charging irradiation amounts for inspection areas on an inspection target. The pre-charging irradiation amounts are based on pattern information for each of the inspection areas. An irradiation unit is provided to control a plurality of first beams to supply the pre-charging irradiation amounts to each of the inspection areas using a corresponding one of the plurality of first beams. After supplying the respective pre-charging irradiation amount to at least one of the inspection areas, irradiation unit controls one of a plurality of second beams to irradiate a pre-charged one of the inspection areas. A generation unit generates images of each of the plurality of inspection areas based on the respective irradiation of the inspection areas with the second beams.

The present disclosure provides an inspection device for use in a mounting system including a mounting device for disposing a component on a board, including a control section configured to extract a mass area included in a captured image resulting from imaging a processing target object where a viscous fluid is formed at a predetermined part, obtain a center of gravity of the mass area so extracted, and determine whether the center of gravity is included in a normal range of the predetermined part as a reference of the captured image to thereby determine whether a bridge has occurred where the viscous fluid is formed over adjacent predetermined parts.

A product defect detection method and apparatus, an electronic device, and a storage medium are provided. A method includes: acquiring a multi-channel image of a target product; inputting the multi-channel image to a defect detection model, wherein the defect detection model includes a plurality of convolutional branches, a merging module and a convolutional headbranch; performing feature extraction on each channel in the multi-channel image by using the plurality of convolutional branches, to obtain a plurality of first characteristic information; merging the plurality of first characteristic information by using the merging module, to obtain second characteristic information; performing feature extraction on the second characteristic information by using the convolutional headbranch, to obtain third characteristic information to be output by the defect detection model; and determining defect information of the target product based on the third characteristic information.

An inspection device includes: a first acquisition unit for acquiring a first defect probability calculated using a first learning model from data regarding an inspection target processed in a first manufacturing process; a second acquisition unit for acquiring a second defect probability calculated using a second learning model from data regarding the inspection target processed in a second manufacturing process after the first manufacturing process; and a determination unit for determining a defect in the inspection target if the acquired second defect probability is greater than or equal to a second threshold value, the determination unit being configured to change at least one of the second learning model and the second threshold value into a condition for enhancing inspection accuracy if the acquired first defect probability is greater than a predetermined first threshold, compared to if the first defect probability is less than or equal to the first threshold value.