Plane detection is a prerequisite for many computer vision tasks. This paper proposes a new method which can automatically detect planes from two projective images. Firstly, we modify Scott’s feature point matching m...Plane detection is a prerequisite for many computer vision tasks. This paper proposes a new method which can automatically detect planes from two projective images. Firstly, we modify Scott’s feature point matching method by post-processing its result with the concept of similarity, and then get the lines matching according to feature points matching based on the approximate invariance of the features’ distribution between two images. Finally, we group all feature points into subsets in terms of their geometric relations with feature lines as initial sets to estimate homography rather than by a random search strategy (like RANSAC) as in most existing methods. The proposed method is especially suitable to detecting planes in man-made scenes. This method is validated on real images.展开更多
The plane metrology using a single uncalibrated image is studied in the paper, and three novel approaches are proposed. The first approach, namely key-line-based method, is an improvement over the widely used key-poin...The plane metrology using a single uncalibrated image is studied in the paper, and three novel approaches are proposed. The first approach, namely key-line-based method, is an improvement over the widely used key-point-based method, which uses line correspondences directly to compute homography between the world plane and its image so as to increase the computational accuracy. The second and third approaches are both based on a pair of vanishing points from two orthogonal sets of parallel lines in the space plane together with two unparallel referential distances, but the two methods deal with the problem in different ways. One is from the algebraic viewpoint which first maps the image points to an affine space via a transformation constructed from the vanishing points, and then computes the metric distance according to the relationship between the affine space and the Euclidean space, while the other is from the geometrical viewpoint based on the invariance of cross ratios. The second and third methods avoid the selection of control points and are widely applicable. In addition, a brief description on how to retrieve other geometrical entities on the space plane, such as distance from a point to a line, angle formed by two lines, etc., is also presented in the paper. Extensive experiments on simulated data as well as on real images show that the first and the second approaches are of better precision and stronger robustness than the key-point-based one and the third one, since these two approaches are fundamentally based on line information.展开更多
文摘Plane detection is a prerequisite for many computer vision tasks. This paper proposes a new method which can automatically detect planes from two projective images. Firstly, we modify Scott’s feature point matching method by post-processing its result with the concept of similarity, and then get the lines matching according to feature points matching based on the approximate invariance of the features’ distribution between two images. Finally, we group all feature points into subsets in terms of their geometric relations with feature lines as initial sets to estimate homography rather than by a random search strategy (like RANSAC) as in most existing methods. The proposed method is especially suitable to detecting planes in man-made scenes. This method is validated on real images.
文摘The plane metrology using a single uncalibrated image is studied in the paper, and three novel approaches are proposed. The first approach, namely key-line-based method, is an improvement over the widely used key-point-based method, which uses line correspondences directly to compute homography between the world plane and its image so as to increase the computational accuracy. The second and third approaches are both based on a pair of vanishing points from two orthogonal sets of parallel lines in the space plane together with two unparallel referential distances, but the two methods deal with the problem in different ways. One is from the algebraic viewpoint which first maps the image points to an affine space via a transformation constructed from the vanishing points, and then computes the metric distance according to the relationship between the affine space and the Euclidean space, while the other is from the geometrical viewpoint based on the invariance of cross ratios. The second and third methods avoid the selection of control points and are widely applicable. In addition, a brief description on how to retrieve other geometrical entities on the space plane, such as distance from a point to a line, angle formed by two lines, etc., is also presented in the paper. Extensive experiments on simulated data as well as on real images show that the first and the second approaches are of better precision and stronger robustness than the key-point-based one and the third one, since these two approaches are fundamentally based on line information.
