Abstract
The method of using a lateral histogram for evaluating the number of holes (e.g., defects) from images is known to be fast but rather inaccurate. Our aim is to propose a method of improving its performance by learning, but keeping the speed of the original method. This task is accomplished by considering a multiclass pattern recognition problem with linearly ordered labels and a loss function, which measures absolute deviations of decisions from true classes.
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Rafajłowicz, E. (2008). Improving the Efficiency of Counting Defects by Learning RBF Nets with MAD Loss. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_15
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DOI: https://doi.org/10.1007/978-3-540-69731-2_15
Publisher Name: Springer, Berlin, Heidelberg
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