On probabilistic methods for object description and classification
This thesis extends the utility of probabilistic methods in two diverse domains: multimodal biometrics and machine inspection. The attraction for this approach is that it is easily understood by those using such a system; however the advantages extend beyond the ease of human utility. Probabilistic measures are ideal for combination since they are guaranteed to be within a fixed range and are generally well scaled. We describe the background to probabilistic techniques and critique common implementations used by practitioners. We then set out our novel probabilistic framework for classification and verification, discussing the various optimisations and placing this framework within a data fusion context. Our work on biometrics describes the complex system we have developed for collection of multimodal biometrics, including collection strategies, system components and the modalities employed. We further examine the performance of multimodal biometrics; particularly examining performance prediction, modality correlation and the use of imbalanced classifiers. We show the benefits from score fused multimodal biometrics, even in the imbalanced case and how the decidability index may be used for optimal weighting and performance prediction. In examining machine inspection we describe in detail the development of a complex system for the automated examination of ophthalmic contact lenses. We demonstrate the performance of this system and describe the benefits that complex image processing techniques and probabilistic methods can bring to this field. We conclude by drawing these two areas together, critically evaluating the work and describing further work that we feel is necessary in the field.