Computer vision includes a variety of tasks of different natures, and there are many applications that have a strong need for knowledge representation and use. Typical knowledge representation methods used in computer vision include frames, rules, logic, constraints, and attributed prototype graphs. Although the advantages of hybrid approaches to knowledge representation have been recognized, no hybrid tool for high-level computer vision is available yet. In this paper we first present a general framework for a hybrid knowledge representation tool. It is based on object-oriented programming and offers distinctive features such as high flexibility, coherence, and a clean integration of a collection of knowledge-based techniques. Then we give a brief overview of our computer vision tool VISTO, which was created along the framework discussed in the first part of the paper. With an application example we illustrate the use of VISTO and the advantages of hybrid knowledge representation in comparison to non-hybrid approaches.
