A Statistical Framework for Long-Range Feature Matching in Uncalibrated Image Mosaicing

The problem considered in this paper is that of estimating the projective transformation between two images in situations where the image motion is large and featurematching is not aided by a proximity heuristic. The overall algorithm designed is based on a multiresolution, multihypothesis scheme, and similarities between tracking and matching through multiple resolution levels are exploited. Two major tools are developed in this paper: (i) a Bayesian framework for incorporating similarity measures of feature correspondences in regression to specify the different levels of confidence in the correspondences; and (ii) a Bayesian version of RANSAC, which is able to utilise prior estimates and matching probabilities. The algorithm is tested on a number of real images with large image motion and promising results were obtained.

• Type: Conference
• Year: 1998
• Where: CVPR
• Authors: Tat-Jen Cham, Roberto Cipolla
• Keywords: Bayesian Framework, Computer Vision, CVPR 1998, Feature Correspondences, Large Image Motion, Multiple Resolution Levels, Overall Algorithm