The invention relates to a method for providing a coating composition-related prediction program, the method comprising: providing a database (204, 904) comprising associations of qualitative and/or quantitative characterizations of coating surfaces and one or more parameters; training a machine learning model for providing a predictive model (M2, M3) having learned to correlate qualitative and/or quantitative characterizations of one or more coating surfaces with one or more of the parameters; and providing a composition-quality-prediction program configured for using the predictive model (M2) for predicting the properties of a coating surface to be produced from one or more input parameters; and/or providing a composition-specification-prediction program configured for using the predictive model (M3) for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to a coating composition predicted to generate a coating surface having the input surface characterizations.

A method for qualitative and/or quantitative characterization of a coating surface is provided, comprising: providing a program recognizing coating surface defect types; determining, by the program, whether a camera(s) coupled to the program is within a predefined distance range and/or within a predefined image acquisition angle range relative to a currently presented coating surface; depending on the determination: generating a feedback signal indicative of whether adjustment of the position of the camera(s) is within predefined distance range and/or within the predefined image acquisition angle range, and/or automatically adjusting the relative distance of the camera and and/or automatically adjusting the angle of the camera; enabling the camera to acquire an image of the coating surface only when the camera(s) is/are within the predefined distance range and/or image acquisition angle range; processing the digital image for recognizing coating surface defects; and outputting a characterization of the coating surface.

A defect detecting device includes an illumination that irradiates a measuring object with illumination light, an imager that images the illumination light reflected by the measuring object, and a detector that detects a defect at a surface of the measuring object based on a captured image obtained by imaging the illumination light by the imager. The captured image includes a plurality of spectral images having different spectral wavelengths, and the detector detects a diffuse reflection region by which the illumination light is diffusely reflected based on the plurality of spectral images, and determines a size of the defect based on the spectral wavelength of the spectral image in which the diffuse reflection region is detected.

A system for cosmetic inspection of a test object is disclosed that includes a movable platform for receiving a test object. The movable platform is capable of positioning the test object within a dome. A plurality of cameras arranged oriented to capture different views of a plurality of surfaces of the test object. A plurality lights arranged are outside the dome, the plurality of lights selectively enabled or disabled according to which of the plurality of surfaces of the test object is to be captured.

There is provided a system and a method comprising obtaining a sequence of a plurality of frames of an area of a specimen, wherein at least one frame of the sequence is transformed with respect to another frame, obtaining a reference frame based at least on a first frame of the sequence, determining, based on the reference frame, a reference pattern, wherein the reference pattern is informative of a structural feature of the specimen in the area, for a given frame of the sequence, determining, based on the given frame, a pattern informative of said structural feature in the area, determining data D.sub.shrinkage informative of an amplitude of a spatial transformation between the reference pattern and the pattern, generating a corrected frame based on said pattern and D.sub.shrinkage and generating an image of the area.

Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify surface irregularities on a veneer surface. A grade is then assigned to the veneer based, at least in part, on the detected irregularities. In one embodiment, the veneer is then provided to an improved veneer stacking system that produces more consistently graded veneer stacks and safer veneer stacks, is less expensive to operate, and is far safer than currently available methods and systems for full veneer sheet, veneer strip, and partial veneer sheet stacking.

A system for defect review and classification is disclosed. The system may include a controller, wherein the controller may be configured to receive one or more training images of a specimen. The one or more training images including a plurality of training defects. The controller may be further configured to apply a plurality of difference filters to the one or more training images, and receive a signal indicative of a classification of a difference filter effectiveness metric for at least a portion of the plurality of difference filters. The controller may be further configured to generate a deep learning network classifier based on the received classification and the attributes of the plurality of training defects. The controller may be further configured to extract convolution layer filters of the deep learning network classifier, and generate one or more difference filter recipes based on the extracted convolution layer filters.

A system, method and computer readable medium for classifying defects, the method comprising: receiving classified first defects, and potential defects, each first and potential defect having values for attributes; processing the first and potential defects to select a subset of the attributes that differentiates the first defects from the potential defects; obtaining first and second functions based on the first defects and potential defects, respectively; obtaining a first threshold for the first function, and a second threshold for a combination of the first and second functions; applying the first function and the second function to each potential defect to obtain first and second scores, respectively; and determining a combined score of the first and second scores; and indicating as a defect of a potentially new type a potential defect when the first score is lower than the first threshold or the combined score exceeds the second threshold.

An image classification method, a computer device, and a medium are disclosed. The method includes: acquiring a middle-level semantic feature of an image to be classified through a visual dictionary; and classifying the image to be classified according to the middle-level semantic feature of the image to be classified using a classification model based on middle-level semantic features.

A system, method and computer software product, the system capable of classifying defects and comprising: an hardware-based GUI component; and a processing and memory circuitry configured to: a. upon obtaining data informative of a plurality of defects and attribute values thereof, using the attribute values to create initial classification of the plurality of defects into a plurality of classes; b. for a given class, presenting to a user, by the hardware-based GUI component, an image of a defect initially classified to the given class with a low likelihood, wherein the image is presented along with images of one or more defects initially classified to the given class with the highest likelihood; and c. subject to confirming by the user, using the hardware-based GUI component, that the at least one defect is to be classified to the given class, indicating the at least one defect as belonging to the given class.