Computer Assisted Problem Solving In Image Analysis

The researcher I user of software for digital image analysis is confronted with huge libraries of subroutines. In order to solve a problem from a set of problems, it is, in general, not clear which subroutines should be selected, with what setting of the parameters, The authors have set out to structure and classify subroutines generally available in image processing and image analysis libraries as a first step in bottom up knowledge engineering. In order to reduce the redundancy in the large sets of subroutines, a virtual image analysis engine has to have a minimum(reduced) instruction set.Computer assisted problem analysis is approached in a top down manner. A PROLOG style specification language is developed, which allows goal directed programming. This means that the problems have to be specified in terms of relations between the components of a model.The language will check whether the number of constraints is sufficient, and if so, will solve the unknown(s). Often a search of problem space has to be performed where an optimisation criterion is required (cost function). The criterion used here is minimum cost! maximum benefit of classification or parameter estimation.