A blackboard-based system which learns recognition rules for
objects from a set of training examples, and then identifies and locates
these objects in test images, is presented. The system is designed to use
data from a feature matcher developed at R.S.R.E. Malvern which finds the
best matches for a set of feature patterns in an image. The feature
patterns are selected to correspond to typical object parts which occur
with relatively consistent spatial relationships and are sufficient to
distinguish the objects to be identified from one another.
The learning element of the system develops two separate sets of
rules, one to identify possible object instances and the other to attach
probabilities to them. The search for possible object instances is
exhaustive; its scale is not great enough for pruning to be necessary.
Separate probabilities are established empirically for all combinations
of features which could represent object instances. As accurate
probabilities cannot be obtained from a set of preselected training
examples, they are updated by feedback from the recognition process.
The incorporation of rule induction and feedback into the blackboard
system is achieved by treating the induced rules as data to be held on a
secondary blackboard. The single recognition knowledge source
effectively contains empty rules which this data can be slotted into,
allowing it to be used to recognise any number of objects - there is no
need to develop a separate knowledge source for each object. Additional
object-specific background information to aid identification can be added
by the user in the form of background checks to be carried out on
The system has been tested using synthetic data, and successfully
identified combinations of geometric shapes (squares, triangles etc.).
Limited tests on photographs of vehicles travelling along a main road
were also performed successfully.
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.