This dissertation gives a general model for the estimation of
shape (image segmentation), appearance, pose (image registration), and
movement (tracking). The model can infer parameters for
multiple objects in a dynamically changing scene.
There are a number of real-world applications.
In particular, in visual tracking, moving the camera to keep
objects of interest in the field of view may
cause the background to move. The objects can
move and deform in three dimensions, but they must be captured in
two-dimensional images.
Each component of the image is represented by
a separate layer: one for the background and a layer for
each foreground object. Each layer has three components: a contour that bounds
the region of the layer, a smooth function that represents the object's
appearance, and a transformation that maps that layer into an image.
The segmentation for each layer is a contour
(embedded as the zero level set of a distance function)
that is the average shape of the object computed from multiple images. The
smooth function associated with a layer approximates the image data inside the
contour, after the contour has been mapped into the image by a
similarity transformation (rigid component) plus a vector field (non-rigid
component). A practical application of having this model is that
one can fix the size of a layer and then construct priors
on both shape and appearance for that layer. These priors are
constructed using principal components analysis (PCA),
which reduces the dimensionality of the
image-approximating smooth function and the vector field (non-rigid
registration) and allows for more accurate modeling of an object
for that layer.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/16305 |
| Date | 09 July 2007 |
| Creators | Jackson, Jeremy D. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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