This thesis is concerned with three-dimensional (3D) reconstruction and point
registration, which are fundamental topics of numerous applications in the area of
computer vision.
First, we propose the multiple epipolar lines (MEL) shape recovery method for
3D reconstruction from an image sequence captured under circular motion. This
method involves recovering the 3D shape by reconstructing a set of 3D rim curves.
The position of each point on a 3D rim curve is estimated by using three or more
views. Two or more of these views are chosen close to each other to guarantee
good image point matching, while one or more views are chosen far from these
views to properly compensate for the error introduced in the triangulation scheme
by the short baseline of the close views. Image point matching among all views
is performed using a new method that suitably combines epipolar geometry and
cross-correlation.
Second, we develop the one line search (OLS) method for estimating the 3D
model of an object from a sequence of images. The recovered object comprises a
set of 3D rim curves. The OLS method determines the image point correspondences
of each 3D point through a single line search along the ray defined by the camera
center and each two-dimensional (2D) point where a photo-consistency index is
maximized. In accordance with the approach, the search area is independently reduced
to a line segment on the number of views. The key advantage of the proposed
method is that only one variable is focused on in defining the corresponding 3D
point, whereas the approaches for multiple-view stereo typically exploit multiple
epipolar lines and hence require multiple variables.
Third, we propose the expectation conditional maximization for point registration
(ECMPR) algorithm to solve the rigid point registration problem by fitting
the problem into the framework of maximum likelihood with missing data. The
unknown correspondences are handled via mixture models. We derive a maximization
criterion based on the expected complete-data log-likelihood. Then, the point
registration problem can be solved by an instance of the expectation conditional
maximization algorithm, that is, the ECMPR algorithm.
Experiments with synthetic and real data are presented in each section. The
proposed approaches provide satisfactory and promising results. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/161580 |
| Date | January 2012 |
| Creators | Zhang, Jian, 张简 |
| Contributors | Chesi, G, Hung, YS |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B48079856 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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