Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
Minimum Spanning Tree Pose Estimation, Parris K. Egbert, Kevin L. Steele
Minimum Spanning Tree Pose Estimation, Parris K. Egbert, Kevin L. Steele
Faculty Publications
The extrinsic camera parameters from video stream images can be accurately estimated by tracking features through the image sequence and using these features to compute parameter estimates. The poses for long video sequences have been estimated in this manner. However, the poses of large sets of still images cannot be estimated using the same strategy because wide-baseline correspondences are not as robust as narrow-baseline feature tracks. Moreover, video pose estimation requires a linear or hierarchically-linear ordering on the images to be calibrated, reducing the image matches to the neighboring video frames. We propose a novel generalization to the linear ordering …
Histogram Matching For Camera Pose Neighbor Selection, Parris K. Egbert, Bryan S. Morse, Kevin L. Steele
Histogram Matching For Camera Pose Neighbor Selection, Parris K. Egbert, Bryan S. Morse, Kevin L. Steele
Faculty Publications
A prerequisite to calibrated camera pose estimation is the construction of a camera neighborhood adjacency graph, a connected graph defining the pose neighbors of the camera set. Pose neighbors to a camera C are images containing sufficient overlap in image content with the image from C that they can be used to correctly estimate the pose of C using structure-from-motion techniques. In a video stream, the camera neighborhood adjacency graph is often a simple connected path; frame poses are only estimated relative to their immediate neighbors. We propose a novel method to build more complex camera adjacency graphs that are …