Multi-camera vision systems: Pose estimation and plenoptic imaging

A method is introduced to track the object's motion and estimate its pose directly from 2D image sequences. Scale-Invariant Feature Transform (SIFT) is used to extract corresponding feature points from image sequences. We demonstrate that pose estimation from the corresponding feature points can be formed as a solution to Sylvester's equation.We then extend this method to estimate the object's pose from multiple cameras. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester's equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester's equation.We also present a general framework for analyzing plenoptic sampling by investigating the spectral analysis of plenoptic imaging. The proposed framework provides a unified representation that generalizes several existing methods for plenoptic sampling, including lightfield and concentric mosaic.