ABSTRACT
This chapter argues that the former, in many ways the more important, is rather poorly understood and modeled, whereas the latter is generally well understood and modeled. It is a selective survey of the role of probabilistic models and statistical techniques in the burgeoning field of image analysis and processing. The chapter introduces several areas of application that engender imaging problems; the principal imaging modalities; and key concepts and terminology. It discusses the role of stochastics in imaging, then illustrates by means of three specific examples: a Poisson process model of positron emission tomography, Markov random field image models and a Poisson process model of laser radar. The chapter emphasizes mathematical formulations and the role of imaging within the context of inference for stochastic processes. It presents some key concepts and issues in image analysis, and to describe some probabilistic/statistical methods applicable to imaging problems.
