A General Preprocessing Method for Improved Performance of Epipolar Geometry Estimation Algorithms

Maria Kushnir            Ilan Shimshoni

Department of Information Systems

University of Haifa, Israel


In this paper a deterministic preprocessing algorithm is presented. It is especially designed to deal with repeated structures and wide baseline image pairs. It generates putative matches and their probabilities. They are then given as input to state-of-the-art epipolar geometry estimation algorithms, improving their results considerably, succeeding on hard cases on which they failed before. The algorithm consists of three steps, whose scope changes from local to global. In the local step, it extracts from a pair of images local features (e.g. SIFT), clustering similar features from each image. The clusters are matched yielding a large number of matches. Then pairs of spatially close features (2keypoint) are matched and ranked by a classifier. The highest ranked 2keypoint-matches are selected. In the global step, fundamental matrices are computed from each two 2keypoint-matches. A matchs score is the number of fundamental matrices, which it supports. This number combined with scores generated by standard methods is given to a classifier to estimate its probability. The ranked matches are given as input to state-of-the-art algorithms such as BEEM, BLOGS and USAC yielding much better results than the original algorithms. Extensive testing was performed on almost 900 image pairs from six publicly available datasets.

Submitted to PAMI

MATLAB demo and Readme

Images used in the paper for more details please contact mariakush@gmail.com