首页> 外文会议>International archives of the photogrammetry, remote sensing and spatial information sciences >ROBUST AND AUTOMATIC VANISHING POINTS DETECTION WITH THEIR UNCERTAINTIES FROM A SINGLE UNCALIBRATED IMAGE,BY PLANES EXTRACTION ON THE UNIT SPHERE
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ROBUST AND AUTOMATIC VANISHING POINTS DETECTION WITH THEIR UNCERTAINTIES FROM A SINGLE UNCALIBRATED IMAGE,BY PLANES EXTRACTION ON THE UNIT SPHERE

机译:通过单位球体上的平面提取,通过从单个未凝固图像的平面提取来检测具有它们的不确定性的鲁棒和自动消失点检测

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This paper deals with the retrieval of vanishing points in uncalibrated images. Many authors did work on that subject in the computer vision field because the vanishing point represents a major information. In our case, starting with this information gives the orientation of the images at the time of the acquisition or the classification of the different directions of parallel lines from an unique view. The goal of this paper is to propose a simple and robust geometry embedded into a larger frame of image work starting with an efficient vanishing point extraction without any prior information about the scene and any knowledge of intrinsic parameters of the optics used.After this fully automatic classification of all segments belonging to the same vanishing point, the error analysis of the vanishing points found gives the covariance matrix on the vanishing point and on the orientation angles of the camera, when using the fact that the 3D directions of lines corresponding to the vanishing points are horizontal or vertical. A validation of estimated parameters with the help of the photo-theodolite has been experimented that demonstrate the interest of the method for real case. The algorithm has been tested on the database of a set of 100 images available on line.
机译:本文涉及未校准图像中的消失点的检索。许多作者确实在计算机视觉领域的那个主题工作,因为消失点代表了主要信息。在我们的情况下,从该信息开始,在获取时或从独特视图中的并行线的不同方向分类,给出图像的定向。本文的目标是提出一种简单且坚固的几何形状,嵌入到更大的图像工作中,从有效的消失点提取开始,没有关于场景的任何先前信息以及所用光学器件的内在参数的任何知识。这种全自动属于相同的消失点的所有段的分类,发现消失点的误差分析在消失点和相机的方向角上给出了协方差矩阵,当使用与消失的线的3D方向相对应的线路点是水平或垂直的。在Photo-TheoGolite的帮助下,估计参数的验证已经过实验,这证明了实际情况的方法的兴趣。该算法已经在数据库上测试了一组100张图像的数据库。

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