Robust Stereo using Correlation Scale Space (bibtex)
by Christian Menard
Abstract:
The stereo analysis method is similar to the human visual system. Due to the way our eyes are positioned and controlled, our brains usually receive similar images of a scene taken from nearby points of the same horizontal level. Therefore the relative position of the images of an object will differ in the two eyes. Our brains are capable of measuring this difference and thus estimating the depth. Stereo analysis tries to imitate this principle. This work contains two complementary and original contributions, one combines stereo techniques with robust statistics and the other solves the correspondence problem in a multi-scale approach using correlation scale-space. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent processing stages. In this work the standard area-based correlation approach is modified so that it can tolerate a significant number of outliers. The approach exhibits a robust behavior not only in the presence of mismatches but also in the case of depth discontinuities. Another central problem in stereo matching using correlation techniques lies in selecting the size of the search window. Small windows contain only a small number of data points, and thus are very sensitive to noise and therefore result in false matches. Whereas large search windows contain data from two or more different objects or surfaces, thus the estimated disparity is not accurate due to different projective distortions in the left and the right image. In this work a new method is proposed providing a continuous scale for the matching process, so that for each region in the stereo pair depending on the local information an optimal scale can be estimated. Results are given on synthetic images for the robust correlation technique. The adaptive matching method using correlation scale-space is tested on both synthetic and real images.
Reference:
Robust Stereo using Correlation Scale Space (Christian Menard), Technical report, PRIP, TU Wien, 1996.
Bibtex Entry:
@TechReport{PTR-Menard96a,
  author =	 "Christian Menard",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-045",
  title =	 "Robust {S}tereo using {C}orrelation {S}cale {S}pace",
  year =	 "1996",
  url =		 "ftp://ftp.prip.tuwien.ac.at/pub/publications/trs/tr45.ps.gz",
  abstract =	 "The stereo analysis method is similar to the human
                  visual system. Due to the way our eyes are
                  positioned and controlled, our brains usually
                  receive similar images of a scene taken from nearby
                  points of the same horizontal level. Therefore the
                  relative position of the images of an object will
                  differ in the two eyes. Our brains are capable of
                  measuring this difference and thus estimating the
                  depth. Stereo analysis tries to imitate this
                  principle. This work contains two complementary and
                  original contributions, one combines stereo
                  techniques with robust statistics and the other
                  solves the correspondence problem in a multi-scale
                  approach using correlation scale-space. Most
                  standard algorithms make unrealistic assumptions
                  about noise distributions, which leads to erroneous
                  results that cannot be corrected in subsequent
                  processing stages. In this work the standard
                  area-based correlation approach is modified so that
                  it can tolerate a significant number of
                  outliers. The approach exhibits a robust behavior
                  not only in the presence of mismatches but also in
                  the case of depth discontinuities. Another central
                  problem in stereo matching using correlation
                  techniques lies in selecting the size of the search
                  window. Small windows contain only a small number of
                  data points, and thus are very sensitive to noise
                  and therefore result in false matches. Whereas large
                  search windows contain data from two or more
                  different objects or surfaces, thus the estimated
                  disparity is not accurate due to different
                  projective distortions in the left and the right
                  image. In this work a new method is proposed
                  providing a continuous scale for the matching
                  process, so that for each region in the stereo pair
                  depending on the local information an optimal scale
                  can be estimated. Results are given on synthetic
                  images for the robust correlation technique. The
                  adaptive matching method using correlation
                  scale-space is tested on both synthetic and real
                  images.",
}
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