Image Processing Reference
Before describing what is meant by the somewhat strange title of this chapter, let us
look at a few examples. In the first example the task is to design an algorithm which
can figure out how many circles are present in the image to the left (see Fig. 7.1 ).
Obviously the answer is three, but how will we make the computer figure this out?
Another example could be to find the position of the person in the image to the
right. How can we make the computer calculate this? The answer to both questions
is twofold. First we have to separate the different objects in the image and then we
have to evaluate which object is the one we are looking for, i.e., circles and humans,
respectively. The former process is known as BLOB extraction and the latter as
BLOB classification. BLOB stands for Binary Large OBject and refers to a group
of connected pixels in a binary image. The term “Large” indicates that only objects
of a certain size are of interest and that “small” binary objects are usually noise.
The title of the chapter refers to analyzing binary images by first extracting the
BLOBs, then representing them compactly, and finally classifying the type of each
BLOB. These three topics are described in more detail below.
The purpose of BLOB extraction is to isolate the BLOBs (objects) in a binary image.
As mentioned above, a BLOB consists of a group of connected pixels. Whether or
not two pixels are connected is defined by the connectivity , that is, which pixels are
neighbors and which are not. The two most often applied types of connectivity are
illustrated in Fig. 7.2 . The 8-connectivity is more accurate than the 4-connectivity,
but the 4-connectivity is often applied since it requires fewer computations, hence
it can process the image faster. The effect of the two different types of connectivity
is illustrated in Fig. 7.2 where the binary images contain either one or two BLOBs
depending on the connectivity.
A number of different algorithms exist for finding the BLOBs and such algo-
rithms are usually referred to as connected component analysis or connected com-
ponent labeling . In the following we describe one of these algorithms known as the
Grass-fire algorithm . We use 4-connectivity for simplicity.