Using statistical models to interpret complex and variable images

Model-based vision has been applied successfully to images of man-made objects. It has proved much more difficult to develop model-based approaches to interpreting images of complex and variable structures such as faces or the internal organs of the human body. The key problem is that of variability. Recent developments have shown that specific patterns of variability in shape and grey-level appearance can be captured by statistical models that can be used directly in image interpretation. The details of the approach are outlined and practical examples from medical image interpretation and face recognition are used to illustrate how previously intractable problems can now be tackled successfully.