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An improved gray weighted method for sub-pixel center extraction of structured light stripe

机译:一种改进的灰度加权方法提取结构光条纹的亚像素中心

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Center extraction of structured light stripe is an essential problem for the development of line structured light sensors (LSLS). To obtain the sub-pixel center coordinates precisely, an improved gray weighted method (IGWM) is proposed with an adaptive sampling region. Firstly, the center of the stripe is computed using gray weighted method (GWM) for each pixel column. Then these center points are fitted using moving least squares algorithm to estimate the tangential vector, the normal vector and the radius of curvature. For each center point, a rectangular region is defined with two sides parallel with the normal vector. The other two sides that parallel with the tangential vector alter their length automatically according to the radius of curvature. After that, the center coordinate at this point is recalculated based on the GWM, but in the normal vector direction and only takes into account the pixels within the rectangular region. The experimental results show that this method is not only suited for the center extraction of smooth laser stripes, but also the ones with sharp corners. The noise can also be obviously suppressed than that of the traditional GWM.
机译:中心提取结构化条纹是开发线性结构光传感器(LSLS)的基本问题。为了精确地获得子像素中心坐标,提出了一种具有自适应采样区域的改进的灰度加权方法(IGWM)。首先,使用灰度加权方法(GWM)为每个像素列计算条纹的中心。然后使用移动最小二乘法对这些中心点进行拟合,以估计切向矢量,法向矢量和曲率半径。对于每个中心点,定义一个矩形区域,其两侧与法线向量平行。与切向矢量平行的其他两个边会根据曲率半径自动更改其长度。之后,基于GWM重新计算此点的中心坐标,但沿法向矢量方向,并且仅考虑矩形区域内的像素。实验结果表明,该方法不仅适用于光滑激光条纹的中心提取,也适合于尖角的条纹提取。与传统GWM相比,噪声也可以得到明显抑制。

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