Object localization refers to the detection, matching and segmentation of objects in
images. The localization model presented in this paper relies on deformable templates
to match objects based on shape alone. The shape structure is captured by a prototype
template consisting of hand-drawn edges and contours representing the object to be
localized. A multistage, multiresolution algorithm is utilized to reduce the computational
intensity of the search. The first stage reduces the physical search space dimensions
using correlation to determine the regions of interest where a match it likely to occur.
The second stage finds approximate matches between the template and target image at
progressively finer resolutions, by attracting the template to salient image features using
Edge Potential Fields. The third stage entails the use of evolutionary optimization to
determine control point placement for a Local Weighted Mean warp, which deforms the
template to fit the object boundaries. Results are presented for a number of applications,
showing the successful localization of various objects. The algorithm’s invariance to
rotation, scale, translation and moderate shape variation of the target objects is clearly
illustrated.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/4662 |
| Date | 12 March 2008 |
| Creators | Spiller, Jonathan Michael |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 3332677 bytes, application/pdf, application/pdf |
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