Abstract
In this paper, we present a framework for the classification of images in surface inspection tasks and address several key aspects of the processing chain from the original image to the final classification result. A major contribution of this paper is a quantitative assessment of how incorporating adaptivity into the feature calculation, the feature pre-processing, and into the classifiers themselves, influences the final image classification performance. Hereby, results achieved on a range of artificial and real-world test data from applications in printing, die-casting, metal processing and food production are presented.
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Eitzinger, C., Heidl, W., Lughofer, E. et al. Assessment of the influence of adaptive components in trainable surface inspection systems. Machine Vision and Applications 21, 613–626 (2010). https://doi.org/10.1007/s00138-009-0211-1
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DOI: https://doi.org/10.1007/s00138-009-0211-1