Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

2.2.3 Feature-based Approach
for Pattern Recognition
The main consideration of this approach is to extract a set of primitive features,
describing the object and to compare it with similar features in the sensory
patterns to be identified. For example, suppose we are interested to identify
whether character ‘H’ is present in the following list of characters (fig. 2.2 (b)).
A H F K L
Fig. 2.2 (b): A list of characters including H.
Now, first the elementary features of ‘H’ such as two parallel lines and one
line intersecting the parallel lines roughly at half of their lengths are detected.
These features together are searched in each of the characters in fig. 2.2 (b).
Fortunately, the second character in the figure approximately contains similar
features and consequently it is the matched pattern.
For matching facial images by the feature-based approach, the features like
the shape of eyes, the distance from the nose tip to the center of each eye, etc.
are first identified from the reference image. These features are then matched
with the corresponding features of the unknown set of images. The image with
the best matched features is then identified. The detailed scheme for image
matching by specialized feature descriptors such as fuzzy moments [5] will be
presented in chapter 23.
2.2.4 The Computational Approach
Though there exist quite a large number of literature on the computational
approach for pattern recognition, the main credit in this field goes to David
Marr. Marr [19] pioneered a new concept of recognizing 3-dimensional objects.
He stressed the need for determining the edges of an object and constructed a
2 ½-D model that carries more information than a 2-D but less than a 3-D image.
An approximate guess about the 3-D object, thus, can be framed from its 2 ½-D
images.
Currently, computer scientists are in favor of a neural model of perception.
According to them, an electrical analogue of the biological neural net can be
trained to recognize 3-D objects from their feature space. A number of training