利用直线聚类和统计分析方法自动地实现对二维图像中提取出的消隐点的位置测算,对后续摄像机标定和三维建模十分有用.在人造建筑物的图像中,存在着许多空间上相互平行的直线,这些直线经过透视投影后将在成像平面上相交于一点,称为消隐点.基于直线倾角聚类的思想,对所有直线按倾角相交得到的待定消隐点进行统计分析处理,实现了消隐点位置的测算.实验表明,由于利用了平面图像中几乎所有的空间结构信息(线条),并采用了聚类和统计分析的数据处理方法,减少了由于噪声或者成像畸变造成的误差,所得结果精度高,算法鲁棒性好,整个测算过程子动完成。

A new method for automatic vanishing point detection on the image plane is proposed, which is based on a combination of line clustering and cross point statistical testing. The knowledge of vanishing point is an important step for oncoming camera self-calibration and 3D reconstruction. Under perspective projection, lines paralleling each other in space will converge to a point called the vanishing point in the image plane. Straight lines are automatically extracted using Burns line finding algorithm, and line clustering is applied to detect groups of lines that intersect on the same vanishing point. The vanishing points are automatically detected and it doesn't need priori information on the geometry of the related lines in object space. The precision of the end point coordinates is set to sub-pixel. The method is applied to the real images and the experimental results illustrate that the algorithm has successful performance and robustness against noise.