In this study the relation between the performance of an imaging unit of a web inspection system and the final image quality is discussed. The basic idea is to analyze the results of the segmentation and feature extraction of defects in sample images as a function of imaging parameters. Determination of the quality of imaging and examples of the performance of a typical imaging unit are reviewed. The effect of the image quality on segmentation of the defects and feature extraction is analyzed in two cases: 1. The detection of small and low-contrast defects in paper inspection and 2. the depth of field considerations in steel inspection. Samples picked from the industrial manufacturing process are imaged using different imaging parameters and the defect areas in the images are segmented in order to illustrate the dependence of the system performance on the quality of imaging. Several segmentation methods are applied. These include direct thresholding, edge-based filtering, matched filtering and morphological filtering. The contrast of certain type of defects can be improved before segmentation by averaging the input data line by line. The signal processing methods presented here are computationally simple due to the need for highspeed real-time implementation in practical inspection.
No abstract
Experience from actual web inspection systems has shown that the quality of the image is crucial for the performance of the whole inspection system. Even the most sophisticated image processing methods or computer hardware cannot compensate for low quality images. In this paper the key characteristics affecting the image formation and test set-ups to measure the numerical values of these characteristics are introduced. The determination of the imaging properties is based on imaging known test targets and subsequent image analysis. Modulation transfer function, vignetting, noise and pixel-to-pixel non-uniformity values for typical image forming systems are given. The sensitivity of these characteristics is estimated by measuring their numerical values as a function of some key imaging parameters like f-number or the spectral range of illumination. The information content of defective surface samples picked from a steel manufacturing process is compared to the results of the test set-up measurements to demonstrate the relation between the quality of the image formation in web inspection and the measured imaging characteristics. As a conclusion, an image formation evaluation procedure is proposed. The possibility to optimize the performance of the image formation using the information from the evaluation procedure is discussed.
The quality of the image is crucial for the performance of a visual surface inspection system. The final image quality is affected by the properties of the illumination and imaging units. In order to evaluate the quality of these units a designer should be able to characterize their performance using quantitative figures of merit.In this paper the suitability of the modulation transfer function (MTF) for the figure of merit in visual web inspection is considered. An uncomplicated test set-up for measuring the system MTF of an imaging unit is presented and MTFs of typical web inspection imaging arrangements are introduced as a function of the f-number of the imaging lens. An appropriate set of defective samples from a paper manufacturing process is collected and imaged using the same arrangements as in the MiT measurements. Defect contrasts and signal-to-noise ratios are determined from the images and the results are compared to the corresponding MiT values.The objective of the study is to demonstrate the relationship between the MiT and the capability of inspection system to detect certain kinds of surface defects. The existence of this relationship is a necessity when MiT is considered as a figure of merit. Based on the experimental results surface defects are classified in two categories: 1 . Defects whose detectivity is critically dependent on the MiT and 2. defects whose detectivity is practically independent of the MiT. As a conclusion, examples are introduced on how the MiT measurement can be used as an imaging system evaluation and design tool.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.