18 December 1996 Comparison of image analysis techniques for defect detection in food-processing applications
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Visual quality control in many food processing operations continues to be a manual, difficult, and tedious task. Computer/machine vision systems offer a solution, but the development of effective algorithms that are able to accommodate the natural variability of food products has proved to be problematic. This paper examines and compares three techniques for processing multi-spectral imagery for these applications. One technique is to use artificial neural networks (ANNs). ANNs have the ability to be fault tolerant when establishing decision surfaces within the test data and can operate in parallel at high speeds -- this makes them ideal for this application. The main drawback of ANNs is their inability to provide a meaningful justification for the decision boundaries they establish when classifying data. Another image processing technique that uses a more deterministic data classification method is vector quantization (VQ). VQ uses a data clustering and splitting algorithm that can be modified to improve speed and accuracy according to the application. In an effort to include all levels of algorithm complexity, a modified thresholding approach is also compared to the more computationally demanding ANN and VQ techniques. The strengths and weaknesses of each of these algorithms are highlighted based on their performance in these domains.
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Timothy Myers Brosnan, Timothy Myers Brosnan, Wayne D. Daley, Wayne D. Daley, Mark J. T. Smith, Mark J. T. Smith, } "Comparison of image analysis techniques for defect detection in food-processing applications", Proc. SPIE 2907, Optics in Agriculture, Forestry, and Biological Processing II, (18 December 1996); doi: 10.1117/12.262869; https://doi.org/10.1117/12.262869

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