In the final part of this book, we discuss four families of models. There is very little new theoretical material; these models are straight applications of the learning and inference techniques introduced in the first nine chapters. Nonetheless, this material addresses some of the most important machine vision applications: shape modeling, face recognition, tracking, and object recognition.

In Chapter 17 we discuss models that characterize the shape of objects. This is a useful goal in itself as knowledge of shape can help localize or segment an object. Furthermore, shape models can be used in combination with models for the RGB values to provide a more accurate generative account of the observed data.

In Chapter 18 we investigate models that distinguish between the identities of objects and the style in which they are observed; a prototypical example of this type of application would be face recognition. Here the goal is to build a generative model of the data that can separate critical information about identity from the irrelevant image changes due to pose, expression and lighting.

In Chapter 19 we discuss a family of models for tracking visual objects through time sequences. These are essentially graphical models based on chains such as those discussed in Chapter 11. However, there are two main differences. First, we focus here on the case where the unknown variable is continuous rather than discrete. Second, we do not usually have the benefit of observing the full sequence; we must make a decision at each time based on information from only the past.