Abstract

Sawmills in Thailand demand an automatic approach to correctly detect rubberwood piths. This is a starting point to maximize the yield of slabs per lumber. Knowing the pith location at both cross-section sides of the lumber makes it possible to rotate the lumber in such a way that both piths are parallel to the saws. Then, a knot, the likely to defect part which runs along the length of the lumber, can be removed. In this paper, we propose an algorithm to accelerate the process of approximating the pith location of rubberwoods. The algorithm employs histogram of oriented gradients (HOG) and a set of relevant histogram bin indices to significantly reduce the number of line segments to be later used in a complex group of lines intersection part of the algorithm. This is in contrast to previously proposed algorithms that employ all edge points to create a huge amount of line segments which consume extremely high processing time. The results confirm that 3,315 times performance is reached at 0.52 reduction of detection error in average compared to the state of the art implementation on a set of 35 cross-section rubberwood images taken by a normal camera.