This dissertation presents the development and performance of a few algorithms used for
automated scene matching. The objective is to recognise and predict the location of a template (reference image) inside a degraded scene image (sensed image). A set of
perspective, projective optical images of relatively well defined man-made objects located in areas of varying background is used as database. Perturbations to the grey levels of the image cause artefacts that easily destroy the unique match location and generate false fixes. Therefore, suitable enhancement and noise removal techniques are applied first. Several different types of features are investigated to decide upon those that are best suited to describe the original content of the scene. Statistical features, such as
invariant moments are chosen for one of the algorithms, Multibcmd Ima^e using Moments
(MBIMOM). The second one, Spatial Multiband Image (SMBI) algorithm, uses the spatial
correlation of the pixels within a neighbourhood as initial descriptive features. Each algorithm uses either Principal Components transform or Maximum Noise Fraction transform for dimensionality and noise reduction. A normalised correlation coefficient of
1.00 was achieved by the SMBI algorithm. The final design of the algorithms is a trade-off between speed and accuracy.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/14110 |
| Date | 12 March 2014 |
| Creators | Danaila, Mariana Liana |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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