A deposit detection device according to an embodiment includes a detection module and an identification module. The detection module detects a small region as a candidate region for a deposit region corresponding to a deposit adhering to an imaging device, based on brightness information for each of small regions into which a predetermined region in an image captured by the imaging device is divided. The identification module identifies the candidate region as the deposit region when undulation change in brightness distribution of pixels included in the candidate region detected by the detection module is within a predetermined range.

Disclosed is a system for analyzing a display device, the system including: an image photographing device configured to photograph an image for inspection output from a display device to be inspected and obtain an RGB value for inspection from the photographed image; and an inspection control unit configured to convert the RGB value for inspection into a CIE XYZ value by using a previously prepared color conversion function, and determine defect of the display device to be inspected by using the converted CIE XYZ value.

An image processing method includes the steps of lighting up at least a part of light emitting units of a light emitting device; capturing a plurality of detection images corresponding to a plurality of sections of the light emitting device respectively, wherein each section includes a plurality of lighted-up light emitting units, each detection image includes a plurality of light spots respectively corresponding to the light emitting units of the associated section, and every two adjacent sections have an overlap area including at least one lighted-up light emitting unit; and stitching the detection images of the adjacent sections together by taking the light spots corresponding to at least one lighted-up light emitting unit of the overlap area as alignment reference spots, so that the light emitting statuses of all the light emitting units are presented by a single image.

In an imaging method, a focal point of a focused optical beam is sequentially mechanically positioned at coarse locations in or on an integrated circuit (IC) wafer or chip. At each coarse location, a two-dimensional (2D) image or mapping tile is acquired by steering the focal point to fine locations on or in the IC wafer or chip using electronic beam steering and, with the focal point positioned at each fine location, acquiring an output signal produced in response to an electrical charge that is optically injected into the IC wafer or chip at the fine location by the focused optical beam. The 2D image or mapping tiles are combined, including stitching together overlapping 2D image or mapping tiles, to generate an image or mapping of the IC wafer or chip. The electronic beam steering may be performed using a galvo mirror. The set of coarse locations may span a three-dimensional (3D) volume.

Various surface and structural defects are currently inspected visually. This method is labor intensive, requiring large maintenance man hours, and is prone to errors. To streamline this process, herein is described an automated inspection system and apparatus based on several optical technologies that drastically reduces inspection time, provides accurate detection of defects, and provides a digital map of the location of defects. The technology uses a sensor that includes a pattern projection generator for generating a pattern image on the structural surface and a camera for detecting the pattern image generated by the pattern projection generator on the structural surface. Furthermore, the technology utilizes an image processing and correction apparatus for performing a pattern image and structural surface defect map correction and generate a distortion corrected defect map for a surface scan area on the structure that is incident on the sensor.

In some aspects, the additive manufacturing system may access, by a processor of an additive manufacturing system, a machine learning model that is trained to identify defects within a build plane. Also, the additive manufacturing system may capture, by an imaging system of the additive manufacturing system, an image of a build plane of the additive manufacturing system. The build plane can contain an object being manufactured through an additive manufacturing process. In addition, the additive manufacturing system may provide, by the processor, the captured image as an input to the machine learning model. Moreover, the additive manufacturing system may receive, by the processor, an output from the machine learning model identifying a defect in the build plane.

A cigarette filter inspection method of inspecting a solid flavor element to be disposed in a void between two filter plugs placed in outer filter wrapper, and the cigarette filter inspection method includes an illumination step of irradiating the void with illumination light, an imaging step of obtaining an inspection image of a region containing the void, a filler detection step of detecting the flavor element based on contrast between the void and the flavor element in the inspection image, and an inspection step of inspecting the flavor element detected in the inspection image.

Apparatus inspects the presence/absence of foreign substance on object having inspection region and non-inspection region arranged outside the inspection region. The apparatus includes sensor for illuminating the object and output, as image, result acquired by detecting light from region including the inspection region, and processor for detecting foreign substance based on inspection region image acquired by excluding non-inspection region image, which is image of the non-inspection region, from the image output from the sensor. The non-inspection region image includes first part generated by light from predetermined part of the non-inspection region of the inspected object and second part whose pixel value is continuous from pixel value of the first part and the processor specifies the second part based on fact that the pixel value of the second part is continuous from that of the first part.

An extraction unit acquires a first difference between a non-defective product image, covering a non-defective product sample, and a reference model and a second difference between a defective product image, covering a defective product sample, and the reference model. A extraction unit extracts, as a potential defect, either the first difference or the second difference, whichever satisfies a particular condition. A calculation unit calculates at least one feature quantity with respect to the potential defect extracted by the extraction unit. When the defective product sample includes a plurality of defective product samples and respective feature quantities of the potential defects extracted from the defective product samples have multiple different values, the calculation unit specifies at least one of the feature quantities that has an Nth largest one of the multiple different values as an indicator. The presentation unit presents the indicator specified by the calculation unit.

A device to detect and analyze defects in a finished surface includes a supporting mechanism, a transmitting mechanism, a detecting mechanism, and a processor. The transmitting mechanism carries and transmits the product. The detecting mechanism includes a detecting frame, a light source assembly. The processor is used to connect to a first camera module and a second camera module, and preprocess the first image and the second image to obtain a detection and analysis of any defects of the front of the product.