The increasing popularity of machine vision based solutions in common applications calls for a structured approach for incorporating the end user’s domain knowledge and limiting the solution’s dependency on expert knowledge. We propose a framework facilitating optimized classification results and will show several approaches in which prior knowledge of the solution is captured in a neural network or in a geometric pattern matcher. The methodology is applied to disc print reading for antibiotic susceptibility testing by disc diffusion. Results show that increased prior knowledge produces better classifiers, and that more thorough optimization is required to increase the accuracy of classifiers which use less prior knowledge.
Klaas Dijkstra, Walter Jansen and Jaap van de Loosdrecht
Published at the European Symposium on Artificial Intelligence and Neural Networks, ESANN 2013