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. © 2008 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Rafajłowicz, E. (2008). Improving the efficiency of counting defects by learning RBF nets with MAD loss. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5097 LNAI, pp. 146–153). https://doi.org/10.1007/978-3-540-69731-2_15
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
