In this dissertation, we conducted a stereoscopic eye tracking experiment using naturalistic stereo images. We analyzed low level 2D and 3D scene
features at binocular fixations and randomly selected places. The results reveal
that humans tend to fixate on regions with higher luminance variations, but
lower disparity variations. Because of the often observed co-occurrence of luminance
and depth changes in natural environments, the dichotomy between
luminance features and disparity features inspired us to study the accurate
statistics of 2D and 3D scene properties.
Using a range map database, we studied the distribution of disparity
in natural scenes. The natural disparity distribution has a high peak at zero,
and heavier tails that are similar to a Laplace distribution. The relevance
of natural disparity distribution to other studies in neurobiology and visual
psychophysics are discussed in detail.
We also studied luminance, range and disparity statistics in natural
scenes using a co-registered luminance-range database. The distributions of
bandpass 2D and 3D scene features can be well modeled by generalized Gaussian
models. There are positive correlations between bandpass luminance and
depth, which can be captured by varying shape parameters in the probability
density functions of the generalized Gaussians. In another study on suprathreshold
luminance and depth discontinuities, we show that observing a significant
luminance edge at a significant depth edge is much more likely than
at homogeneous depth surfaces. It is also true that a significant depth edge happens at a significant luminance edge with a greater probability than at homogeneous luminance regions. Again, the dependency between luminance and
depth discontinuities can be modeled successfully by generalized Gaussians. We applied our statistical models in 3D natural scenes to stereo correspondence.
A Bayesian framework is proposed to incorporate the bandpass disparity prior, and the luminance-disparity dependency in the likelihood function.
We compared our algorithm with a classical simulated annealing method based on heuristically defined energy functions. The computed disparity maps show great improvements both perceptually and objectively. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-08-1978 |
| Date | 13 December 2010 |
| Creators | Liu, Yang, 1976- |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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