The invention relates to a non-invasive process for evaluating the quality of one or more dense connective tissue(s) in a patient, comprising the following steps: a) Analyzing the profile of the microrelief of a cutaneous replica of a portion of the skin of said patient by at least one of the following step: a1. visually assessing on picture(s) of said cutaneous replica the line shape and the anisotropy of the lines; and/or a2. Determining, on picture(s) of said cutaneous replica, the roughness index of the microrelief with an optical sensor, b) identifying cutaneous replica of stage 1, representative of healthy skins, and cutaneous replica of stage 2 representative of altered skins, a cutaneous replica of stage 2 being indicative of low quality of the one or more dense connective tissue(s) in the patients body.

The present disclosure discloses a method for detecting a wafer backside defect, comprising: Step 1, providing a signal database comprising signal data corresponding to various different defects, the defects comprising convex defects and concave defects, the signal data reflecting 3D information of the corresponding defect; Step 2, performing backside scanning on a tested wafer by using oblique incident light, and collecting corresponding emitted and scattered light data; and Step 3, comparing the collected emitted and scattered light data with the signal data, and fitting a defect 3D distribution map of the backside of the tested wafer. The present disclosure can test the height or depth of a wafer backside defect and form a 3D distribution map of the wafer backside defect, which is beneficial for analyzing the source of the wafer backside defect and processing it in time, reducing the troubleshooting time and improving the product yield.

A method for detecting, on the finish of containers, defects in a horizontal mold seal of the container includes the steps of disposing the container between a light source and a camera and ensuring the rotation of the container on itself according to one rotational revolution. The camera acquires, at each increment of rotation of the container, an image so that the number of images per rotational revolution is greater than 36. The images captured for each container are analyzed such that the profile of the finish edge is detected in each image, the profiles of the finish edge of the images are compared with a reference profile of the finish edge so as to detect deviations between these profiles, and a defect in the horizontal mold seal for a container is detected when at least one image of said container has a deviation.

A method for optically inspecting a mold (10) for manufacturing ophthalmic lenses such as contact lenses for possible mold defects, including: generating a set of images of the mold (10) for different azimuthal illumination angles (1, 9) using an illumination system (20) and an imaging system (30), the latter being aligned such that its focal plane cuts through the mold (10) at a specific axial position along a center axis of the mold (10); generating a focal plane image by averaging pixelwise over the set of images after having masked out in each image those regions that include direct specular reflections from the mold (10); repeating the previous steps for one or a plurality of different axial positions of the focal plane such as to generate a plurality of different focal plane images; identifying one or more image features in the plurality of focal plane images indicative for a possible mold defect; determining for each identified image feature in which focal plane image the identified image feature appears sharpest; generating for each identified image feature a respective image section out of the respective sharpest focal plane containing the image feature; and generating a composed dark field image of the mold (10) by composing the respective image sections for each identified image feature, thus enabling to determine as to whether the possible defects of the mold (10) still allow the mold (10) to be used.

A convolutional neural network may be trained to inspect subjects such as carbon fiber propellers for surface flaws or other damage. The convolutional neural network may be trained using images of damaged and undamaged subjects. The damaged subjects may be damaged authentically during operation or artificially by manual or automated means. Additionally, images of undamaged subjects may be synthetically altered to depict damages, and such images may be used to train the convolutional neural network. Images of damaged and undamaged subjects may be captured for training or inspection purposes by an imaging system having cameras aligned substantially perpendicular to subjects and planar light sources aligned to project light upon the subjects in a manner that minimizes shadows and specular reflections. Once the classifier is trained, patches of an image of a subject may be provided to the classifier, which may predict whether such patches depict damage to the subject.

A method for automatic defect classification, the method may include (i) acquiring, by a first camera, at least one first image of at least one area of an object; (ii) processing the at least one first image to detect a group of suspected defects within the at least one area; (iii) performing a first classification process for initially classifying the group of suspected defects; (iii) determining whether a first subgroup of the suspected defects requires additional information from a second camera for a completion of a classification; (iv) when determining that the first subgroup of the suspected defects requires additional information from the second camera then: (a) acquiring second images, by the second camera, of the first subgroup of the suspected defects; and (b) performing a second classification process for classifying the first subgroup of suspected defects.

Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with an inspection system using first and second optical states of the inspection system. The first and second optical states are defined by different values for at least one optical parameter of the inspection system. The method also includes generating first image data for the wafer using the output generated using the first optical state and second image data for the wafer using the output generated using the second optical state. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.

An automated optical inspection (AOI) equipment with adjustable image capturing combination and an image capturing combination adjusting method thereof are provided. The automated optical inspection equipment includes at least one image capturing apparatus, at least one moveable light source and a device-under-test (DUT) movement mechanism. A control circuit moves DUT, selects one light source, and controls the light source to move or rotate. The control circuit is capable of performing the capturing operation by switching multiple image capturing combinations sequentially. Each of the image capturing combinations records the image capturing apparatus, the position and angle of the DUT, the light source and its position and angle relative to DUT. The positions or angles of capturing combinations are different.

Accordingly, a large number of the image capturing results can be obtained for the DUT quickly and automatically.

A method for optically inspecting a mold (10) for manufacturing ophthalmic lenses such as contact lenses for possible mold defects, including: generating a set of images of the mold (10) for different azimuthal illumination angles (1, 9) using an illumination system (20) and an imaging system (30), the latter being aligned such that its focal plane cuts through the mold (10) at a specific axial position along a center axis of the mold (10); generating a focal plane image by averaging pixelwise over the set of images after having masked out in each image those regions that include direct specular reflections from the mold (10); repeating the previous steps for one or a plurality of different axial positions of the focal plane such as to generate a plurality of different focal plane images; identifying one or more image features in the plurality of focal plane images indicative for a possible mold defect; determining for each identified image feature in which focal plane image the identified image feature appears sharpest; generating for each identified image feature a respective image section out of the respective sharpest focal plane containing the image feature; and generating a composed dark field image of the mold (10) by composing the respective image sections for each identified image feature, thus enabling to determine as to whether the possible defects of the mold (10) still allow the mold (10) to be used.

This invention provides a processing apparatus for determining a state of secular change of a deformation of a construction, comprising a selection unit that selects, as a target for determination of secular change, at least a portion of deformations from among a plurality of deformations included in a first image at a first time period, on the basis of at least one of information relating to deformations, information relating to the construction, user selection, or a shape and a relative positional relationship of two or more deformations; a first determination unit that determines a deformation corresponding to a selected deformation among a plurality of deformations included in a second image at a second time period; and a second determination unit that determines a state of secular change between a selected deformation and a deformation determined by the first determination unit.