An integrated system for automatic forming, picking, and inspection of a grinding wheel mesh piece and a method thereof, including a visual inspection system (1), a conveying system (2), a cutting system (3), and a picking system (4); the conveying system (2) is used to precisely control a conveying action of a cutting section conveying platform (51) and a picking section conveying platform (52), and the visual inspection system (1) is used to acquire an image of a grinding wheel mesh cloth, establish virtual origin coordinates of a cutting layout and center coordinates of the grinding wheel mesh piece after cutting, recognize defects of the grinding wheel mesh cloth, and calibrate qualified center coordinates and unqualified center coordinates; the cutting system (3) is used to cut the grinding wheel mesh cloth moved to the cutting section conveying platform (51) to obtain a circular grinding wheel mesh piece.

Monitoring operations of a polishing system includes obtaining a time-based sequence of reference images of a component of the polishing system performing operations during a test operation of the polishing system, receiving from a camera a time-based sequence of monitoring images of an equivalent component of an equivalent polishing system performing operations during polishing of a substrate, determining a difference value for the time-based sequence of monitoring images by comparing the time-based sequence of reference images to the time-based sequence of monitoring image using an image processing algorithm, determining whether the difference value exceeds a threshold, and in response to determining the difference value exceeds the threshold, indicating an excursion.

Method for detecting a defect in a zone of interest of an optical lens, the method including: an image reception step, during which a plurality of images is received, each image includes a view of the zone of interest in front of a plurality of specific patterns, each specific pattern including a bright area and a dark area, and at least one image received is saturated in light intensity; a sampling step, during which each image of the plurality of images are sampled based on a common sampling pattern; a recombination step, during which a recombined image of the zone of interest is determined based on the common sampling pattern; and a defect detection step, during which a defect is detected in the zone of interest of the optical lens based on an analysis of the recombined image.

An information processing device is a device used in a mounting device that collects a component and arranges the component on a substrate. In this information processing device, a control section acquires a captured image including a component, sets multiple detection lines for detecting a brightness difference of the component with respect to the acquired captured image in the component, and obtains a reference position of an outer edge portion from the multiple detection lines. Next, the control section uses a predetermined weight coefficient of which weight tends to decrease as a distance from the reference position increases to obtain a candidate value obtained by adding the weight coefficient to one or more outer edge portion candidates existing on the detection line. Subsequently, the control section selects a position of the outer edge portion existing on the detection line based on the obtained candidate value.

An operation determination apparatus includes an image obtainer that obtains a captured image of a worker gripping a component and performing an operation of attaching the component to a workpiece, a component detector that detects a component in the captured image obtained by the image obtainer, a grip determiner that determines, based on a detection result from the component detector, whether the worker is gripping a component and whether the component gripped by the worker is a component to be attached to the workpiece in the operation, and a notification controller that controls a predetermined notification to be provided in response to the grip determiner determining that the component gripped by the worker is not the component to be attached to the workpiece in the operation.

The present disclosure discloses an Internet of Things (IoT) system for industrial data processing, a control method, and a storage medium, and provides a technical solution for a timely adjustment of production line parameters according to image information generated during a machine vision data collection on the production line. Through a difference of the image information corresponding to different process operations, a processing situation of different process operations may be obtained, so that more accurate adjustment of the production line parameter may be achieved without increasing the system complexity, thereby effectively reducing a development cost of the IoT, and increasing accuracy of the intelligent manufacturing control.

The coordinate system fixing unit uses the displacement of an object under measurement between photographed images in chronological order to generate fixed-coordinate chronological images. The displacement calculation unit uses the fixed-coordinate chronological images to calculate a two-dimensional spatial distribution of the displacement of the surface of the object under measurement. The displacement difference calculation unit calculates a two-dimensional displacement difference distribution by removing an error component from the two-dimensional spatial distribution. The depth movement amount calculation unit calculates a depth movement amount from the two-dimensional displacement difference distribution. The displacement separation unit calculates in-plane displacement from the two-dimensional displacement difference distribution. The determination unit uses the in-plane displacement and/or the depth movement amount to determine whether there is an abnormality in the object under measurement.

The present invention relates to a method of detecting an edge (or a contour line) of a pattern, which is formed on a workpiece (e.g., a wafer or a mask) for use in manufacturing of semiconductor, from an image generated by a scanning electron microscope. The pattern-edge detection method includes: generating an objective image of a target pattern formed on a workpiece; generating a feature vector representing features of each pixel constituting the objective image; inputting the feature vector to a model constructed by machine learning; outputting, from the model, a determination result indicating whether the pixel having the feature vector is an edge pixel or a non-edge pixel; and connecting a plurality of pixels, each having a feature vector that has obtained a determination result indicating an edge pixel, with a line to generate a virtual edge.

An automatic measuring system containing configurable integrated circuits is able to process information via captured images. The automatic measuring system includes a metering instrument, a camera, a recognition module, and a localization module. The metering instrument has at least one display for visually displaying a number and measures the amount of measurable substance or resources (i.e., electricity and water) consumed. The camera captures an image of the number representing at least a portion the amount of measurable substance. The recognition module is operable to generate a value in response to the image and the coordinates wherein the coordinates are used to decode the image via restoring captured image to the original readout counter value. The localization module is removably or remotely coupled to the camera and operable to generate the coordinates in accordance with the image captured by the camera.

A method and apparatus for performing automated supervision and inspection of an assembly process. The method is implemented using a computer system. Sensor data is generated at an assembly site using a sensor system positioned relative to the assembly site. A three-dimensional global map for the assembly site and an assembly being built at the assembly site is generated using the sensor data. A current stage of an assembly process for building an assembly at the assembly site is identified using the three-dimensional global map. A context for the current stage is identified. A quality report for the assembly is generated based on the three-dimensional global map and the context for the current stage.