conclusions

• The proposed colour image segmentation method achieved a high defect detection rate (95%) with a low false positive rate (6%) on images of wood boards.
• The FMMIS method is based on the original FMM, but with a new learning algorithm specially adapted for image segmentation tasks.
• The FMMIS method combines clustering with region-based techniques to obtain a substantially different method than the original Simpson’s FMM.
• The results show that significant improvements have been obtained in comparison to previous work.