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Very common uses of AAM's are for medical image analysis and face recognitionA.1. Active appearance models possess traits that make them robust and effective in the biological domain, whereas industrial inspection, for example, presents some inherently different problems. These problems are often solved more rapidly by other approaches that are based on lower-level knowledge about the image contents. Since a broad range of tasks are performed in industrial inspection, however, it is also valid to assume that the suitability of top-down approach is irrespective of the problem. Nonetheless, there has been some successful application of these methods to the analysis and segmentation in printed boards. Kestra have been doing some successful work in this domain, though it is not a main-stream application of statistical models as yet.
In order to visualise biological shapes and full appearances, a model which handles anatomical variability and change needs to be used. It must account for natural or pathological changes such as the change in form of organs (atlases for different pathologies are suggested in []). Greater variability can be encountered when aligned images are obtained from different subjects in a population (inter-subject), the same subject at different time instances (or different sites) or when having to account for movement such as the that which occurs due to respiration, the cardiac cycle and so forth. A separate case to consider is multi-modal imaging which will not be explained in any detail although it is a quickly-developing area.
For an excellent overview on many of the different image analysis techniques, the book from Sonka et al. [] is a valuable source. For a good review of model-based image analysis, papers from Cootes et al. are an even better source. Related and similar insights can be derived from work partially based on concepts such as snakes, bending-energy and active contours. Such conceptual approaches, are closely-related to active shape models. Sonka et al. discuss all of these in depth.