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 match’s
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.
MATLAB demo and Readme
Images
used in the paper for more details please contact mariakush@gmail.com