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Research on Preprocessing of Color Image for Vision based Mobile Robot Navigation

机译:基于视觉的移动机器人导航彩色图像预处理研究

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There are special requirements on image processing in speed and robustness for vision based mobile robot navigation. But mobile robot captured scene color image?s processing has a high computational complexity and low robustness. To reduce data volume and improve robustness, a novel preprocessing method on color image for mobile robot navigation based on principle of threshold segmentation in HSV color space is proposed. Firstly, color image changed from GRB to HSV. Thinking of H component is instable when saturation level is too low, so the principle for single threshold gray image segmentation be used to choose a threshold T on S and divide S into high saturation and low saturation region, we can use H as segmentation basis in high saturation region and V in low saturation region. Then project H component to high saturation region and V component to low saturation region, H and V is stretched to different scope after projection. So, the S component contains not only color information but also gray information and reduces color image from three-dimentions to two-dimentions. Experimental results show that this method has more significant effects in improving the processing speed and robustness of illumination variation and shadows.
机译:对于基于视觉的移动机器人导航,图像处理在速度和鲁棒性方面有特殊要求。但是移动机器人捕获的场景彩色图像的处理具有较高的计算复杂度和较低的鲁棒性。为了减少数据量并提高鲁棒性,提出了一种基于阈值分割原理在HSV颜色空间中对移动机器人导航彩色图像进行预处理的新方法。首先,彩色图像从GRB变为HSV。当饱和度水平太低时,H分量的思想就不稳定,因此采用单阈值灰度图像分割的原理在S上选择阈值T并将S分为高饱和度和低饱和度区域,我们可以将H用作分割的基础高饱和区,V处于低饱和区。然后将H分量投影到高饱和度区域,将V分量投影到低饱和度区域,投影后将H和V拉伸到不同的范围。因此,S分量不仅包含颜色信息,还包含灰度信息,并将彩色图像从3维减少到2维。实验结果表明,该方法在提高光照变化和阴影的处理速度和鲁棒性方面具有更显着的效果。

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