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A Fast Algorithm to Approximate the Pith Location of Rubberwood Timber from a Normal Camera Image

机译:从普通摄像机图像中估算橡胶木的髓位的快速算法

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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.
机译:泰国的锯木厂需要一种自动检测橡胶木髓的方法。这是最大程度提高每块木材板材产量的起点。知道木料在木材的两个横截面侧的位置,可以旋转木料,使两个木料都平行于锯。然后,可以去除沿木材长度方向延伸的可能有缺陷的部分的结。在本文中,我们提出了一种算法来加快估计橡胶木的髓位的过程。该算法采用定向梯度直方图(HOG)和一组相关的直方图bin索引,以显着减少稍后在算法的复杂线交叉部分中使用的线段数量。这与先前提出的使用所有边缘点来创建大量线段的算法相反,这些线段消耗了非常长的处理时间。结果证实,与现有技术在由普通相机拍摄的一组35幅横断面橡胶木图像上相比,与现有技术相比,平均检测误差降低了0.52倍,性能达到了3315倍。

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