It is necessary for a correla tor to be invariant to all distortions of a reference object while still being able to maintain good discrimination between similar objects. A filter must thus strike a compromise between the conflicting requirements of in-class distortion tolerance and out-of-class discrimination. A bandpass filter is therefore ideal; the high frequency response being attenuated to provide less sensitivity to in-class variations while the low frequencies are removed since they compromise the discriminatory ability of the filter. This study proposes a nonlinear bandpass pre-processing operation applied to both the input and the reference images prior to the correlation operation, i.e. in the space domain rather than to the spectrum. This operation is shown to result in greater tolerance to in-class variation while maintaining an excellent discrimination ability. Additionally, the introduction of non-linearity is shown to provide greater robustness in the filter response to noise and background clutter in the input scene. Utilizing the good performance of this new filter and in order to provide improved inter-class tolerance for a higher distortion range, the proposed nonlinear bandpass operation is incorporated in the synthesis of a modified Synthetic Discriminant Function (SDF) filter by applying the non-linearity to each of the bandpass filtered training set images comprising the SDF. This SDP filter is demonstrated to produce a sharp peak, and show excellent discrimination ability, while maintaining invariance to out-of-plane rotation over a distortion range of up to 90 degrees. The processing sequence of this filter is suitable for implementation by a hybrid digital/optical arrangement in which the input image is wavelet filtered in real-time by a digital signal processor (DSP). The filter performance is investigated with both input image and filter plane quantization to the limit of binary amplitude and phase, and hence it is suitable for implementation with current frame-rate Spatial Light Modulators (SLM) technology.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:264595 |
| Date | January 1999 |
| Creators | Jamal-Aldin, Lamia S. |
| Publisher | University of Sussex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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