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6multidimensional observations at each pixel.1.4 OverviewI try to make only minimal assumptions about the imaging method, but somedecisions are needed. For instance, raw data from PET images are modeled muchbetter by Poisson mixtures than by Gaussian mixtures, and vice versa for aerialphotography. In general, different features may be distinguished by color or texture.I do not consider texture differences here. The examples and discussionwill focus on greyscale images, though these methods can be extended to color ormultispectral images.Chapter 2 presents a method for automatic detection of curves in spatial pointprocesses; this method uses principal curves to model features and the BIC tochoose the amount of smoothing. Chapter 3 discusses marginal segmentationmethods, which I use to find an initial segmentation of the image. Chapter 4presents two models for autoregressive dependence, the AR(1) model and theraster scan autoregression (RSA) model, and discusses how BIC can be adjustedto accomodate these models. In chapter 5, I discuss a Markov random field modelfor images, and I describe an algorithm for automatic, unsupervised image segmentation.Examples follow at the end of the chapter. Conclusions and furtherdiscussion are given in chapter 6. A discussion of the software developed with thisdissertation is given in Appendix A.