Cast Shadow Resistant Ground Plane Detection in Single Image

Xiaoyan Xu, Xiaoming Liu, Chang-jia Liu, Yuan Ling
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Abstract

Ground plane detection is useful for vision navigation to robots and autonomous vehicles. Cast shadow on the ground plane is a challenging issue that may cause the detection fail. In this paper, we present a cast shadow resistant ground plane detection approach from a single color image. We first derive an initial ground plane using geometric layout method. We then apply shadow invariant transform on the roughly detected shadow edges to get a gray-scale intrinsic image. The final shadow resistant ground plane result is obtained by employing region growth on the initial seed region in the shadow-free intrinsic image. The approach proposed here does not need priori assumption such as calibration or landmark. Experimental results show the method works for different scenes.
单幅图像抗投影地平面检测
地平面检测对机器人和自动驾驶汽车的视觉导航非常有用。地平面上的投影是一个具有挑战性的问题,可能导致检测失败。本文提出了一种基于单色图像的抗投影地平面检测方法。首先用几何布局法求出初始地平面。然后对粗略检测到的阴影边缘进行阴影不变性变换,得到灰度图像。通过在无阴影本征图像的初始种子区域上进行区域生长,得到最终的抗阴影地平面结果。本文提出的方法不需要先验假设,如校准或地标。实验结果表明,该方法适用于不同场景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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