<title>Non-monotonic model-based system for scene interpretation</title>

Abstract

Model-based scene interpretation systems can use non-monotonic reasoning to resolve many of the inherent ambiguities and uncertainties present in real world image data. Model-based systems perform object recognition by efficiently matching features extracted from image data to corresponding features in known object models. This matching process is based on the accuracy of the extracted features. It is typically the case, however, that these features are themselves uncertain. This uncertainty is present because the image processing algorithms used for feature extraction do not consider global context, are noise sensitive, and use scene dependent parameters. Non-monotonic reasoning can resolve these uncertainties by identifying global inconsistencies and providing the means to recover from them. It does this by retracting invalid deductions based on the underlying causes of inconsistencies as they arise during the model matching process. A non-monotonic system can also predict the existence of additional features to complete a partial match.