Chapter . Image segmentation using fuzzy min-max neural networks for wood defect detection

G.A. Ruz[a], [b] and P.A. Estévez[a]

Abstract

In this work a colour image segmentation method for wood surface defect detection is presented. In an automated visual inspection system for wood boards, the image segmentation task aims to obtain a high defect detection rate with a low false positive rate, i.e., clear wood areas identified as defect regions. The proposed method is called FMMIS (Fuzzy Min-Max neural network for Image Segmentation). The FMMIS method grows boxes from a set of seed pixels, yielding the minimum bounded rectangle (MBR) for each defect present in the wood board image. The FMMIS method was applied to a set of 900 colour images of radiata ...

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