Abstract:
Pattern recognition constitutes a very vast area in image processing with applications in
face recognition, eye recognition, neural networks etc.
Pattern recognition is a central problem in many technical fields in which we allow ‘n’
dimensional objects to be sufficiently measured by means of a data-processing system so
that the data-processing system can identify with the highest possible precision to which
n-dimensional object the respective pattern must be assigned. Highly precise pattern
recognition of n-dimensional objects would make it possible, for example, to steer
vehicles of any kind automatically, so that accidents due to human error can be largely
prevented. It would also be possible to recognize the handwriting of any given person
automatically and with high precision. The manufacture of automated machines or robots
equipped with intelligent sensor systems would be no problem if highly precise pattern
recognition systems are available.
Numerous pattern-recognition techniques are already known from the prior art. One
disadvantage of the known techniques, however, is that each is usable only for special
types of objects. The general usability of these known techniques is therefore greatly
restricted.
Numerous methodologies are dependant on the application. One of the most common
applications is detection and recognition of sign boards, which is mostly used in
automated robotic vehicles. An approach for detection and recognition of stop sign is
presented in this project. Both color and gray scale models are used to make the
recognition process as accurate as possible and to get an optimal balance in the result.
The stop sign is detected by means of rules that restrict color and shape of the sign board.
It is then recognized by applying Hough’s transform, which is a structural recognition
approach. The number of sides of the stop sign and its color are being used as the primary
feature set in the project. This method can be easily used for different class of sign’s with
some variation in the recognition process.
