A new technique for iconic hypothesis verification in model-based vision systems has been developed, which enhances the resolution of the problem of three-dimensional object recognition in two-dimensional scenes. This thesis investigates an iconic feature-matching approach to verification, in which two-dimensional image features are predicted from a specific view of a three-dimensional geometric model, and these features are matched directly to the unprocessed image data. This solves the crucial image to model registration problem. The iconic matching approach solves two of the major disadvantages of the usual symbolic matching method; where symbolic image constructs are compared with symbolic model data. The symbolic description of image features is not robust, and detailed matches cannot be made, as much of the original data has been lost. The investigation of iconic verification has been split into two parts. Firstly individual features are matched. Secondly the results from these are aggregated into a model match score. For the first stage four iconic evaluators have been developed and compared. These predictive evaluators are designed to assess the "edge-ness" of a small patch of an image. The advantage of one of these techniques over its equivalent data-driven approach is shown. The complete verification procedure aggregates the image-specific iconic feature evaluation scores. The iconic matching technique has been tested in the domain of car recognition in outdoor scene images. Its sensitivity in images containing a great deal of distracting noise has been very encouraging. There are however many application areas for this research. Iconic matching can be used to track both individual features and entire objects, for example in successive frames of a sequence of images over time
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:278094 |
| Date | January 1990 |
| Creators | Brisdon, Kay |
| Publisher | University of Reading |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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