An abstract representation of geometric knowledge for object classification
We propose a new approach to object classification based on the idea of geometric abstraction. A class of objects is described by means of a model which specifies the shape invariants common to all the members of the class. A model is a list of geometric constraints fixing the ranges in which local shape features can vary. We also propose an efficient algorithm for constraint satisfaction and we show the computational and descriptive advantages of our proposal with respect to other classification approaches based on average prototype models.
abstraction object classification constraint satisfaction geometric knowledge representation polygonal approximation