The Weizmann Institute of Science
Faculty of Mathematics and Computer Science
Vision and Robotics Seminar
Ilan Shimshoni
will speak on
Guided Sampling via Weak Motion Models and
Outlier Sample Generation for Epipolar Geometry
Abstract:
The problem of automatic robust estimation of the epipolar geometry in cases where the correspondences
are contaminated with a high percentage of outliers is addressed. This situation often occurs when the
images have undergone a significant deformation, either due to large rotation or wide baseline of the
cameras. An accelerated algorithm for the identification of the false matches between the views is
presented. The algorithm generates a set of weak motion models (WMMs). Each WMM roughly approximates
the motion of correspondences from one image to the other. The algorithm represents the distribution
of the median of the geometric distances of a correspondence to the WMMs as a mixture model of outlier
correspondences and inlier correspondences. The algorithm generates an outlier correspondence sample
from the data. This sample is used to estimate the outlier rate and to estimate the outlier pdf. Using
these two pdfs the probability that each correspondence is an inlier is estimated. These probabilities
enable to guide the sampling. In the RANSAC process this guided sampling accelerates the search process.
The resulting algorithm when tested on real images achieves a speedup of one or two orders of magnitude!
In addition I will also present a new algorithm from the RANSAC family which is a major improvement of the
pbM estimator algorithm of Chen and Meer (ICCV 2003) and a general technique to compare the runtime of
algorithms from the RANSAC family. Using this method we show that our algorithm runs 2-3 times faster
than the original algorithm prosposed by Chen and Meer and other algorithms from the RANSAC family.
Joint work with Liran Goshen and Stas Rozenfeld.
The lecture will take place in the
Lecture Hall, Room 1, Ziskind Building
on Thursday, December 23, 2004
at noon