Abstract In this paper, we use local feature transformations estimated in the matching process as initial seeds for 2D homography estimation. The number of testing hypotheses is equal to the number of matches, naturally enabling a full search over the hypothesis space. Using this property, we develop an iterative algorithm that clusters the matches under the common 2D homography into one group, i.e., features on a common plane. Our clustering algorithm is less affected by the proportion of inliers and as few as two features on the common plane can be clustered together; thus, the algorithm robustly detects multiple dominant scene planes. The knowledge of the dominant planes is used for robust fundamental matrix computation in the presence of quasi-degenerate data.
Simultaneous Plane Extraction and 2D Homography Estimation Using Local Feature Transformations
Computer Vision – ACCV 2007 ; 4 ; 269-278
Lecture Notes in Computer Science ; 4844 , 4
2007-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Feature Transformation , Epipolar Geometry , Viewpoint Change , Common Plane , Maximally Stable Extremal Region Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Biometrics , Algorithm Analysis and Problem Complexity
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