Meanwhile, we suggest that the so-called output-dependent HMMs could be represented in a state-dependent manner, and vice versa, essentially by application of Bayes' theorem. Finally, in Chapter \ref{ch:img}, we present discriminative approaches to histogram-based image thresholding, in which the optimal threshold is derived from the maximum likelihood based on the conditional distribution $p(y|x)$ of $y$, the class indicator of a grey level $x$, given $x$. The discriminative approaches can be regarded as discriminative extensions of the traditional generative approaches to thresholding, such as Otsu's method~\citep{Otsu:79} and Kittler and Illingworth's minimum error thresholding (MET)~\citep{Kittler:86}. As illustrations, we develop discriminative versions of Otsu's method and MET by using discriminant functions corresponding to the original methods to represent $p(y|x)$. These two discriminative thresholding approaches are compared with their original counterparts on selecting thresholds for a variety of histograms of mixture distributions. Results show that the discriminative Otsu method consistently provides relatively good performance. Although being of higher computational complexity than the original methods in parameter estimation, its robustness and model simplicity can justify the discriminative Otsu method for scenarios in which the risk of model mis-specification is high and the computation is not demanding.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:500804 |
| Date | January 2008 |
| Creators | Xue, Jinghao |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/272/ |
Page generated in 0.0014 seconds