Comparison of soybean wax firelogs, commercial firelogs, and firewood with respect to air emissions.

A crucial step in developing automated visual inspection systems for wood boards is image segmentation, which aims to achieve a high defect detection rate with a low false positive rate (clear wood areas identified as defect areas). In this study, a neurofuzzy color image segmentation method for wood surface defect detection is proposed. The method is called fuzzy min-max neural network for image segmentation (FMMIS). The FMMIS method grows boxes from a set of pixels called seeds, to find the minimum bounded rectangle (MBR) for each defect present in the wood board image. An automatic method to select seeds from defective regions as starting points to FMMIS is also presented. The FMMIS method was applied to a set of 900 images of radiata pine boards, which included samples from the following 10 categories of defects: birdseye and freckle, bark and pitch pockets, wane, splits, blue stain, stain, pith, dead knots, live knots, and holes. The FMMIS achieved a defect detection rate of 95 percent on the test set, with only 6 percent of false positives. To measure the quality of segmentation, the area recognition rate (ARR) criterion was computed using as a reference the manually placed MBR for each defect. The ARR achieved 94.4 percent on the test set. Also a relative index was used to compare the quality of segmentation between FMMIS and the segmentation module of a previously developed system, based on histogram thresholding. The results show that FMMIS allows us to obtain significant improvements compared with previous work.