A Modular Computer Vision System for Picture Segmentation and Interpretation

Authors:
Martin D. Levine;Samir I. Shaheen
Affiliations:
SENIOR MEMBER, IEEE, Department of Electrical Engineering, McGill University, Montreal, P.Q., Canada.;Department of Electronics and Communications Engineering, Cairo University, Cairo, Egypt/ Department of Electrical Engineering, McGill University, Montreal, P.Q., Canada.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1981

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Original Contribution: Fuzzy-set-based hierarchical networks for information fusion in computer vision

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Abstract

The objective of a computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This paper proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short-term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long-term memory (LTM) contains a detailed model of the scene under consideration. A collection of analysis processors, each of which is specialized for a particular task, can communicate with both of these memories. The information in the LTM remains unchanged during the analysis, while the STM is being continually updated and revised by the appropriate processors. The latter may be conceived of as being activated by certain data conditions in the STM, and using the information in both the LTM and STM to alter the status of the STM.