A deposit detection device according to an embodiment includes a detection module, a determination module, and an identification module. The detection module detects a deposit region corresponding to a deposit adhering to an imaging device, based on brightness information of an image captured by the imaging device. The determination module determines whether variation in brightness information in a predetermined region of the image is within a predetermined range, in a period after a vehicle is stopped in a state in which the deposit region is detected by the detection module. The identification module identifies brightness information serving as a determination criterion of the deposit region when the determination module determines that the variation in brightness information is within a predetermined range.

A method for semiconductor wafer inspection is provided. The method includes the following operations. The semiconductor wafer is scanned to acquire a scanned map, wherein the semiconductor wafer is patterned according to a design map having a programmed defect. The design map and the scanned map are transformed to a transformed inspection map according to the location of the programmed defect on the design map and the location of the programmed defect on the scanned map. The system of semiconductor wafer inspection is also provided.

An extraction section extracts, from each of a plurality of non-defective product images which show the appearance of the inspection target determined to be a non-defective product, the non-defective vector representing a feature of each non-defective product image. A generation section generates a transformation matrix by using a plurality of non-defective product vectors extracted by the extraction section. The transformation matrix is a matrix representing sequentially performing first mapping for mapping the feature vector to a feature space and second mapping for mapping a result of the first mapping to the whole space to which the feature vector belongs. An adjusting section adjusts each element of the transformation matrix generated by the generation section by using, as learning data, the feature vector extracted from a pseudo defect image.

Provided are a structure management device, a structure management method, and a structure management program capable of suppressing deterioration of image quality of a mapped captured image and easily performing comparison with past inspection result.

A structure management device includes an image acquiring unit (401) that acquires an image group, a damage detecting unit (403) that analyzes images in the image group to detect damage of the structure, a three-dimensional design data acquiring unit (407) that acquires three-dimensional design data indicating the structure, a combined information calculating unit (409) that tracks point group data of feature points common in the overlap area between the images in the image group to calculate combined information including a camera position and a camera posture in a case of capturing the image by the camera and a three-dimensional position of the point group data, and a mapping unit (411) that maps the detected damage on a surface of the structure indicated by the acquired three-dimensional design data based on the combined information calculated by the combined information calculating unit.

The disclosure herein generally relates to image processing, and, more particularly, to a method and system for impurity detection using multi-modal image processing. This system uses a combination of polarization data, and at least one of a depth data and an RGB image data to perform the impurity material detection. The system uses a graph fusion based approach while processing the captured images to detect presence of the impurity material, and accordingly alert the user.

An inspection apparatus configured to inspect a target for defects, including: an image capturing unit capable of capturing an image of the target as image information having color information including RGB values; and a determination unit configured to determine presence or absence of defects in the target based on the color information of the image information of the image captured by the image capturing unit, wherein the determination unit is configured to define, for each pixel, criteria for determining presence or absence of defects in each pixel of the image information, based on the color information in a defect-free region of the target captured by the image capturing unit, and to filter all pixels in the image information of the image captured by the image capturing unit so as to determine presence or absence of defects in each pixel, based on the defined criteria.

A semiconductor defect inspection apparatus for inspecting a specimen including a semiconductor substrate having a surface on which a predetermined pattern is formed, includes an excitation light irradiator, a polarization converter, a detector, and a defect analysis detector. The excitation light irradiator irradiates the specimen with excitation light along an optical path from the irradiator to the specimen and such that the excitation light is obliquely incident at a predetermined incident angle. The first polarization converter is disposed in the optical path, and converts the excitation light into s-polarized light. The detector detects photoluminescence light generated from the specimen when the excitation light is incident on the specimen. The defect analysis detector detects a dislocation defect by analyzing a photoluminescence image obtained by photoelectrically converting the photoluminescence light.

A semiconductor defect inspection apparatus for inspecting a specimen including a semiconductor substrate having a surface on which a predetermined pattern is formed, includes an excitation light irradiator, a polarization converter, a detector, and a defect analysis detector. The excitation light irradiator irradiates the specimen with excitation light along an optical path from the irradiator to the specimen and such that the excitation light is obliquely incident at a predetermined incident angle. The first polarization converter is disposed in the optical path, and converts the excitation light into s-polarized light. The detector detects photoluminescence light generated from the specimen when the excitation light is incident on the specimen. The defect analysis detector detects a dislocation defect by analyzing a photoluminescence image obtained by photoelectrically converting the photoluminescence light.

A measurement device includes: an obtainer that obtains a plurality of images of a support member that movably supports a structure, the plurality of images being captured at mutually different times while the structure is subjected to varying loads; and a measurer that measures displacement of the support member based on the plurality of images obtained by the obtainer.

A device and a method for monitoring and/or inspecting moving material webs. The method includes taking a first picture of a first portion of a material web at a first point in time with a camera, which has a matrix chip with a binning function, and taking a second picture of a second portion of the material web at a second point in time with the camera. A first binning step level is used for the first picture and a second binning step level is used for the second picture. The first number of pixels which are in each case grouped together is higher or lower than the second number of pixels which are in each case grouped together, as a result of which a physical zoom function is achieved for the second picture.