This thesis deals with the problem of tracking highly deformable
objects in the presence of noise, clutter and occlusions. The
contributions of this thesis are threefold:
A novel technique is proposed to perform filtering on
an infinite dimensional space of curves for the purpose of tracking
deforming objects. The algorithm combines the advantages of particle
filter and geometric active contours to track deformable objects in
the presence of noise and clutter.
Shape information is quite useful in tracking deformable
objects, especially if the objects under consideration get partially
occluded. A nonlinear technique to perform shape analysis, called
kernelized locally linear embedding, is proposed. Furthermore, a new
algebraic method is proposed to compute the pre-image of the
projection in the context of kernel PCA. This is further utilized in
a parametric method to perform segmentation of medical images in the
kernel PCA basis.
The above mentioned shape learning methods are then incorporated into a
generalized tracking algorithm to provide dynamic shape prior for
tracking highly deformable objects. The tracker can also model image
information like intensity moments or the output of a feature
detector and can handle vector-valued (color) images.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/13996 |
| Date | 23 October 2006 |
| Creators | Rathi, Yogesh |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 2828552 bytes, application/pdf |
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