Feature Extraction & Image Processing, 2nd Edition

(a) Original image (b) Edge image (c) Valley image (d) Peak image

Figure 6.2 Energy fields over whole face image (Benn, 1999)

processing speed achieved via optical interconnect, but computers like that are not ready yet

(on our budgets at least). We can reduce the number of combinations by introducing constraints

on the relative size and position of the shapes, e.g. the circle should lie wholly within the

parabolas, but this will not reduce the number of combinations much. We can seek two alter-

natives: one is to use optimization techniques. The original approach (Yuille, 1991) favoured

the use of gradient descent techniques; currently, the genetic algorithm approach (Goldberg,

1988) seems to be most favoured and this has been shown to good effect for deformable

template eye extraction on a database of 1000 faces (Benn, 1999) (this is the source of the

images shown here). The alternative is to seek a different technique that uses fewer param-

eters. This is where we move to snakes, which are a much more popular approach. These

snakes evolve a set of points (a contour) to match the image data, rather than evolving

a shape.

6.3 Active contours (snakes)

6.3.1 Basics

Active contours or snakes (Kass et al., 1988) are a completely different approach to feature

extraction. An active contour is a set of points that aims to enclose a target feature, the feature

to be extracted. It is a bit like using a balloon to ‘find’ a shape: the balloon is placed outside

the shape, enclosing it. Then by taking air out of the balloon, making it smaller, the shape is

found when the balloon stops shrinking, when it fits the target shape. By this manner, active

contours arrange a set of points so as to describe a target feature, by enclosing it. Snakes are

quite recent compared with many computer vision techniques and their original formulation

was as an interactive extraction process, although they are now usually deployed for automatic

feature extraction.

An initial contour is placed outside the target feature, and is then evolved so as to enclose

it. The process is illustrated in Figure 6.3, where the target feature is the perimeter of the iris.

First, an initial contour is placed outside the iris (Figure 6.3a). The contour is then minimized

to find a new contour which shrinks so as to be closer to the iris (Figure 6.3b). After seven

iterations, the contour points can be seen to match the iris perimeter well (Figure 6.3d).

244 Feature Extraction and Image Processing