BLOB Analysis - Introduction to Video and Image Processing - page 110

7.4 Further Information

The grass-fire algorithm can be modified to also operate on gray-scale and color
images. The first modification is that the algorithm does not scan the entire image,
but instead starts at a so-called seed point often defined interactively by a user. The
second modification is that an object pixel is a pixel within a certain gray-scale or
color range. The range can for example be defined as the value of the seed point

a
small value. A more robust approach is to define the range based on the statistics of
the pixels located in the vicinity of the seed point, see Appendix C. The effect this
algorithm will have is that a region centered around the seed point will be selected.
One might think of the algorithm as a combination of thresholding and connected
component analysis. The algorithm is known as region growing and can for example
be applied to remove the red-eye effect in pictures.
The grass-fire algorithm is not the only connected component analysis algorithm
that exits. But no matter which algorithm is used it is very often combined with
the feature extraction process since both need to process each pixel in a BLOB.
Combining them will speed up the system. Many other features than those described
in this chapter exist, especially more advanced shape features such as Hu moments.
Furthermore, many new features can be defined/optimized with respect to a concrete
application.
A common question when doing BLOB classification is whether a simple box
classifier is sufficient. The answer depends on the application. If the feature vectors
of the non-object BLOBs and the object BLOBs are far apart in the feature space,
then the exact position and shape of the decision region is not critical and hence a
box classifier will suffice. This is the situation in Fig. 7.7 . The accuracy of the box
classifier goes down as the feature vectors becomes similar. This is illustrated in
Fig. 7.9 where it can be seen that the weighted Euclidean distance classifier outper-
forms the box classifier.
Another line of argumentation is that the number of parameters needed to be
defined in the box classifier (the shape of the rectangle) increases as the number of
feature increase. In the weighted Euclidean distance classifier only one parameter
(a threshold on the distance) has to be decided independent on how many features
are used.
Sometimes we will have features that are dependent. Dependency means that if
we know something about one feature we can say something about another feature.
If for example we as features have area and perimeter, then it is very likely that
the value of the perimeter increases as the area increases. Dependency in data can
result in the point cloud having an orientation that is neither vertical nor horizontal,
see Fig. 7.9 (c). In these cases both the box classifier and the weighted Euclidean
distance classifier will fail. Instead we must use the Mahalanobis distance classifier .
It is a statistical classifier measuring the distance between an unknown feature vector
and the prototype. So like the two other statistical classifiers presented above it only
requires one parameter to be defined no matter how many features are used. In fact,
the Euclidean distance classifier and the weighted Euclidean distance classifier are
both special cases on the Mahalanobis distance classifier. In Fig. 7.9 (c) the decision

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