29-09-2022 дата публикации
Номер: US20220309634A1
A system and method evaluate defects in printed images. A target image, which has been captured of a printed image, is processed to identify defects, where present, which do not occur in a source image from which the printed image was generated. A trained classification model predicts a defect class for respective regions of the target image, each of the defect classes being drawn from a predefined set of defect classes. For at least one of the identified defects, a measure of severity of the defect is determined, such as a size of the defect. A decision on the acceptability of the printed image is made, based on the measure of severity of the at least one defect and the predicted defect class of a respective one of the regions in which the defect occurs. 1. A method for evaluating defects in printed images , comprising:receiving a target image, captured of a printed image;identifying one or more defects in the target image, where present, which do not occur in a source image from which the printed image was generated;with a trained classification model, segmenting the source image into regions and predicting a defect class for each of the regions of the source image and respective regions of the target image, each of the defect classes being drawn from a predefined set of at least three defect classes, each of the defect classes being associated with a threshold measure of severity;for at least one of the identified defects, determining a measure of severity of the defect;making a decision on an acceptability of the printed image, based on the measure of severity of the at least one defect and the threshold measure of severity associated with the predicted defect class of a respective one of the regions of the target image in which the defect occurs.2. The method of claim 1 , wherein the receiving of the target image comprises receiving a scanned image of the printed image and aligning the scanned image with the source image to identify the target image.3. The ...
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