Feature Extraction and Image Processing

Flexible shape extraction (snakes and other techniques) 243

again represents a shape as a set of landmark points and uses a set of training data to

establish the potential range of variation in the shape. One major difference is that AAMs

explicitly include texture and updates model parameters to move landmark points closer to

image points by matching texture in an iterative search process. The essential differences

between ASMs and AAMs include:

1. that ASMs use texture information local to a point, whereas AAMs use texture information

in a whole region;

2. that ASMs seek to minimise the distance between model points and the corresponding

image points, whereas AAMs seek to minimise distance between a synthesised model

and a target image;

3. that AAMs ...