ANALYSIS AND REPRESENTATION OF COMPUTER VISION SYSTEMS BY THE OBJECT-PROCESS METHODOLOGY

Computer vision involves a host of problems, algorithms and techniques dealing with all aspects of capturing scenes and converting them into meaningful interpretations. A computer vision system may include a pattern recognition subsystem and be itself embedded within a more complex system. Machine vision systems feature a combination of complexity on one hand and a balance between structure and behavior on the other hand. Analysis and design of computer vision svstems calls therefore for a metKodology that represents &ually well structure and behavior within a unified- frame-- ~- of reference and has adequate tools for complexity management. This paper discusses the object-process analysis (OPA) as an approach to tackle this task. Following the introduction of the basic OPA principles, we use the Image Understanding Environment (IUE) Project documentation to demonstrate the principles, use and the benefits of the methodology. The result is a series of consistent, inter-related object-process diagrams that gradually expose the details of the system. Complexity is managed through visibility control, which is obtained by a host of options for scaling object process diagrams. The ease of application of object-process analysis to the case in point suggests that it can be successfully applied to analyze, understand and wmmunicate complex software systems such as the IUE Project