Structural analysis and classification of patterns
This work concerns the development of efficient methods for the
recognition of binary picture objects, with a view to applications such
as automatic location or inspection of industrial components and optical
character recognition. Most common pattern recognition schemes attempt
to overcome the high dimensionality of an input picture by taking
feature measurements from it, which it is hoped will retain sufficient
'useful' information to enable correct classification to be made. If
features are chosen on an 'ad hoc' basis as is often the case, there may
be a loss of 'useful' information in the transformation from picture to
feature space. Thus the error rate of the subsequent classifier may be
increased. Furthermore, there is no easy way of estimating the increase
in error rate.
One way of ensuring zero loss of information in the transformation to
feature space is to use a reversible transformation, which by suitable
coding removes much of the redundancy in the original picture. A number
of possible picture coding methods are examined. Of these, skeleton
coding is chosen as being most suitable.
The technique adopted thus involves the reduction of a binary picture
object to a skeleton. By means of topological analysis and limb
measurements, a feature vector is then produced suitable for subsequent
classification by a nearest neighbour classifier.
In order to be of practical use, any pattern recognition scheme must
be capable of efficient implementation on readily available hardware.
Thus much consideration has been given to the implementation of the
algorithms described. In addition a study has been made of various types
of hardware for picture processing.
During the course of the work, a number of useful hardware and
software tools were developed. These are described in some detail, and
include an interactive system for the development of picture processing