Two Vanishing Points Error Estimation Based on Line Clustering and Condition Adjustment with Parameters

In close-range digital photogrammetry and computer vision, a major challenge is the automation of 3D reconstruction from 2D-images. And single image calibration is a fundamental task in these areas for research. It is known that camera parameters can be recovered by the vanishing points of three orthogonal directions. However, three reliable and well-distributed vanishing points are not always available. Therefore, how to estimate the error of two vanishing points is very significant for us to analyze the precision of camera calibration. New methods for vanishing point detection and error estimation are presented, which can be illustrated as follows. Firstly, the line clustering, which parallel to object lines and correspond to the vanishing points, are detected based on RANSAC (Random Sample Consensus). Secondly, "condition adjustment with parameters" is utilized to estimate a nonlinear error equation. Thirdly, the error of vanishing point is expressed by error ellipse that is derived by co-factor matrix according to adjustment principle. Finally, experimental results of vanishing points coordinates and their errors are shown and analyzed.

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Using Line Segment Clustering to Detect Vanishing Point

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.1553 ◽

2011 ◽

Vol 268-270 ◽

pp. 1553-1558

Author(s):

Fang Wan ◽

Fei Deng

Keyword(s):

Principal Direction ◽

Detection Algorithm ◽

Vanishing Point ◽

Vanishing Points ◽

Line Segments ◽

Single View ◽

Concept Space ◽

Basic Work ◽

Vanishing Point Detection ◽

On Line

Vanishing point detection is a basic work in camera self-calibration, single view reconstruction and series of images matching. Our research is based on line segments clustering method. First, we scan the image with edge detection algorithm for series of line segments. Then, we construct a similar concept space to classify the segments according to the vector distances. At last, we can use each cluster of the line segments to estimate the responsible vanishing point. For the clusters of the line segments indicate the main direction in multiple lines, the detected vanishing points are principal direction points. From the experiments, we approve our algorithm can acquire accurate position of vanishing points in short time.

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Vanishing Point Detection based on Line Set Optimization

Journal of Physics Conference Series ◽

10.1088/1742-6596/1748/3/032052 ◽

2021 ◽

Vol 1748 ◽

pp. 032052

Author(s):

Xiaoyun An ◽

Tongzhou Zhao ◽

Shanju Jin ◽

Chengwan Yang

Keyword(s):

Set Optimization ◽

Vanishing Point ◽

Vanishing Point Detection ◽

On Line ◽

Point Detection

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A New Vanishing Point Detection Algorithm for Orthogonal Directions

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.110 ◽

2012 ◽

Vol 490-495 ◽

pp. 110-114

Author(s):

Mao Li Fu ◽

Can Zhao ◽

Jun Ting Cheng

Keyword(s):

Detection Algorithm ◽

Vanishing Point ◽

It Use ◽

Orthogonal Direction ◽

Vanishing Points ◽

Marquardt Algorithm ◽

Vanishing Point Detection ◽

End Use ◽

Straight Lines ◽

Point Detection

This paper presents the orthogonal direction vanishing points detection algorithm for scene image. It use the Hough transform to detect straight lines in the image, then it based on RANSAC robust framework to calculate the initial value, the end use of Levenberg-Marquardt algorithm for nonlinear optimization iteration to strike the final vanishing point. The experiments result shows that the algorithm can not only quickly detect scene orthogonal direction vanishing points, but also high-precision and that respected this method suitable for use in a scene with large buildings or hyperopia scene.

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