A rapid, automated flaw segmentation method using morphological reconstruction to grade wood flooring

Abstract

Region-Growing Algorithms (RGAs) are used to grade the quality of manufactured wood flooring. Traditional RGAs are hampered by problems of long segmentation time and low inspection accuracy caused by neighborhood search. We used morphological reconstruction with the R component to construct a novel flaw segmentation method. We initially designed two template images for low and high thresholds, and these were used for seed optimization and inflation growth, respectively. Then the extraction of the flaw skeleton from the low threshold image was realized by applying the erosion termination rules. The seeds in the flaw skeleton were optimized by the pruning method. The geodesic inflection was applied by the high threshold template to realize rapid growth of the flaw area in the floor plate, and region filling and pruning operations were applied for margin optimization. Experiments were conducted on 512×512, 256×256 and 128×128 pixel sizes, respectively. The 256×256 pixel size proved superior in time-consumption at 0.06 s with accuracy of 100%. But with the region-growing method the same process took 0.22 s with accuracy of 70%. Compared with RGA, our proposed method can realize more accurate segmentation, and the speed and accuracy of segmentation can satisfy the requirements for on-line grading of wood flooring.