期刊
MACHINE VISION AND APPLICATIONS
卷 21, 期 5, 页码 613-626出版社
SPRINGER
DOI: 10.1007/s00138-009-0211-1
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资金
- EC [016429]
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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