Variable illumination and invariant features for detecting and classifying varnish defects

This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn\u27t provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction

Variable illumination and invariant features for detecting and classifying varnish defects

This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn't provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction

Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.M.S.Committee Chair: Tequila Harris; Committee Member: Levent Degertekin; Committee Member: Wayne Dale

Non-destructive evaluation of white striping and microbial spoilage of Broiler Breast Meat using structured-illumination reflectance imaging

Manual inspection is a prevailing practice for quality assessment of poultry meat, but it is labor-intensive, tedious, and subjective. This thesis aims to assess the efficacy of an emerging structured illumination reflectance imaging (SIRI) technique with machine learning approaches for assessing WS and microbial spoilage in broiler breast meat. Broiler breast meat samples were imaged by an in house-assembled SIRI platform under sinusoidal illumination. In first experiment, handcrafted texture features were extracted from direct component (DC, corresponding to conventional uniform illumination) and amplitude component (AC, unique to the use of sinusoidal illumination) images retrieved from raw SIRI pattern images build linear discriminant analysis (LDA) models for classifying normal and defective samples. A further validation experiment was performed using deep learning as a feature extractor followed by LDA. The third experiment was on microbial spoilage assessment of broiler meat, deep learning models were used to extract features from DC and AC images builds on classifiers. Overall, this research has demonstrated consistent improvements of AC over DC images in assessing WS and spoilage of broiler meat and that SIRI is a promising tool for poultry meat quality detection