Image Processing Reference
In-Depth Information
6
Flexible shape extraction
(snakes and other
techniques)
6.1
Overview
The previous chapter covered finding shapes by matching. This implies knowledge of a
model (mathematical or template) of the target shape (feature). The shape is
fixed
in that
it is flexible only in terms of the parameters that define the shape, or the parameters that
define a template's appearance. Sometimes, however, it is not possible to model a shape
with sufficient accuracy, or to provide a template of the target as needed for the GHT. It
might be that the exact shape is
unknown
or it might be that the perturbation of that shape
is
impossible
to parameterise. In this case, we seek techniques that can
evolve
to the target
solution, or adapt their result to the data. This implies the use of flexible shape formulations.
This chapter presents four techniques that can be used to find flexible shapes in images.
These are summarised in Table 6.1 and can be distinguished by the matching functional
used to indicate the extent of match between image data and a shape. If the shape is flexible
or
deformable
, so as to match the image data, we have a
deformable template
. This is
where we shall start. Later, we shall move to techniques that are called
snakes
, because of
their movement. We shall explain two different implementations of the snake model. The
first one is based on discrete minimisation and the second one on finite element analysis.
We shall also look at finding shapes by the
symmetry
of their appearance. This technique
finds any symmetric shape by gathering evidence by considering features between pairs of
points. Finally, we shall consider approaches that use the
statistics
of a shape's possible
appearance to control selection of the final shape, called
active shape models
.
Table 6.1
Overview of Chapter 6
Deformable templates
Discrete minimisation
Snakes
Flexible shape extraction
Finite elements
Symmetry operations
Active shape models
