A defect inspecting system includes a detector configured to image a sample and a host control device that acquires an inspection image including a defect and a plurality of reference images not including a defect site and generates a pseudo defect image by editing a predetermined reference image among the plurality of acquired reference images. An initial parameter is determined with which the pseudo defect site is detectable from the pseudo defect image. The host control device acquires a defect candidate site from the inspection image using the initial parameter, estimates a high-quality image from an image of a site corresponding to the defect candidate site using the parameter acquired in image quality enhancement, and specifies an actual defect site in the inspection image by executing defect discrimination. A parameter is determined with which a site close to the specified actual defect site is detectable using the inspection image.

The present invention relates to a device (1) for detecting surface defects in an object (100), for example an industrial gasket. The detection device comprises lighting means (2) configured to illuminate said object with a first light radiation (L.sub.1) having a first lighting direction (D.sub.1) or with a second light radiation (L.sub.2) having a second lighting direction (D.sub.2). According to the invention, the detection device comprises acquisition means (30) configured to acquire a plurality of B/W images of said object, when illuminated by said lighting means.

An information processing apparatus (10) is for supporting work by a user who uses drawings for a plant. The information processing apparatus (10) includes a controller (15). The controller (15) is configured to convert a drawing including elements configuring the plant into an abstract model represented by element information indicating the elements and connection information indicating a connection relationship between the elements. The controller (15) is configured to generate display information, when it is judged that a difference exists between one abstract model based on one drawing and another abstract model based on another drawing, for displaying the differing portion in a different form than another portion.

A method and device for testing product quality are disclosed. The method for testing product quality comprises: acquiring an image of a product to be tested; testing the image by using a pre-trained neural network model to obtain a testing result output by the neural network model; when the testing result indicates that the product to be tested is a defective product, performing a secondary judgment on the testing result according to position information of defective feature pixels in the image in the testing result, and determining whether the product to be tested is qualified according to a secondary judgment result. The method has high test accuracy, ensures the quality of product and facilitates reducing the labor cost of test.

A learning data generation device that can generate learning data suitable for learning of an identification model. The learning data generation device has a function of cutting out part of first image data as second image data, a function of generating a two-dimensional graphic corresponding to the area of the second image data and representing a pseudo defect, a function of generating third image data by combining the second image data and the two-dimensional graphic, and a function of assigning a label corresponding to the two-dimensional graphic to the third image data. By using the third image data for learning of the identification model, a highly accurate identification model can be generated.

A method is specified for determining a size of a defect occurring in a surface region of a component while a surface modification process is performed on the surface region. The method includes identifying an occurrence of a defect occurring in a surface region of a component on a basis of a set of images and determining a size of the defect in a separate method step from the occurrence of the defect identified. In addition, an apparatus and a computer program are specified for determining a size of a defect occurring in a surface region of a component while a surface modification process is performed on the surface region.

A process includes receiving a target quality value, receiving a measured quality value, receiving a source quality value, and sending a source control instruction. The source control instruction is based at least in part on the target quality value, the measured quality value, and the source quality value. The target quality value, the measured quality value, the source quality value, and the source control instruction are communicated via the communication port. The measured quality value is generated by an inspection device configured to inspect a sample. The source quality value is associated with a quality level of a first group of samples. The target quality value indicates a desired quality value of an output group of samples. The source control instruction causes a source selecting device to select one of a plurality of groups of samples, each group having identified quality characteristics.

A reticle inspection system may include two or more inspection tools to generate two or more sets of inspection images for characterizing a reticle, where the two or more inspection tools include at least one reticle inspection tool providing inspection images of the reticle. The reticle inspection system may further include a controller to correlate data from the two or more sets of inspection images to positions on the reticle, detect one or more defects of interest on the reticle with the correlated data as inputs to a multi-input defect detection model, and output defect data associated with the defects of interest.

A machine vision-based method and system to facilitate the unloading of a pile of cartons within a work cell are provided. The method includes the step of providing at least one 3-D or depth sensor having a field of view at the work cell. Each sensor has a set of radiation sensing elements which detect reflected, projected radiation to obtain 3-D sensor data. The 3-D sensor data including a plurality of pixels. For each possible pixel location and each possible carton orientation, the method includes generating a hypothesis that a carton with a known structure appears at that pixel location with that container orientation to obtain a plurality of hypotheses. The method further includes ranking the plurality of hypotheses. The step of ranking includes calculating a surprisal for each of the hypotheses to obtain a plurality of surprisals. The step of ranking is based on the surprisals of the hypotheses.

A connected ecosystem for a laboratory environment comprises an electronic lab notebook, and instrumented biosafety cabinet, and one or more sensing vessels containing cell cultures. The electronic lab notebook interfaces with the instrumented biosafety cabinet to provide instructions, guidance, and monitoring of a user during the set up of the experimental protocol and to receive commands from the user via one of several input modalities. After the experimental protocol has been set up in the instrumented biosafety cabinet, cell cultures may be moved to an incubator where the connected ecosystem may provide automatic monitoring of the cultures. The automatic monitoring is provided by sensors integrated into cell culture vessels and supplemented by images of the cell cultures captured by a camera. The user may be informed of deviations from expected results detected based on the automatic monitoring.