An Overview of Advances of Pattern Recognition Systems in Computer Vision

[attachment=16336]
First of all, let's give a tentative answer to the following question: what is pattern
recognition (PR)? Among all the possible existing answers, that which we consider being
the best adapted to the situation and to the concern of this chapter is: "pattern recognition is
the scientific discipline of machine learning (or artificial intelligence) that aims at classifying
data (patterns) into a number of categories or classes". But what is a pattern?


1.1 Statistical approach
Typically, statistical PRSs are based on statistics and probabilities. In these systems, features
are converted to numbers which are placed into a vector to represent the pattern. This
approach is most intensively used in practice because it is the simplest to handle.
In this approach, patterns to be classified are represented by a set of features defining a
specific multidimensional vector: by doing so, each pattern is represented by a point in the
multidimensional features space. To compare patterns, this approach uses measures by
observing distances between points in this statistical space. For more details and deeper
considerations on this approach, one can refer to (Jain, 2000) that presents a review of
statistical pattern recognition approaches.


Syntactic approach
Also called structural PRSs, these systems are based on the relation between features. In this
approach, patterns are represented by structures which can take into account more complex
relations between features than numerical feature vectors used in statistical PRSs
(Venguerov & Cunningham, 1998). Patterns are described in hierarchical structure
composed of sub-structures composed themselves of smaller sub-structures.