Model-Constrained Recognition Of Line Drawings Of Objects By Production Rules And Hierarchical Structures
A model-constrained rule-based object recognition system for line drawings is considered. The system is composed of three subsystems: preprocessing (segmentation), feature extraction, and pattern matching. The subsystems and the processes within each system are arranged in a hierarchical structure. The actual implementation is centered around the ideas of view-angle-independent object recognition and robustness with incomplete or noisy line drawings of objects. The line drawing of a scene containing simple objects is first segmented into independent objects using a set of heuristics stored in the knowledge base. The segmented object is then mapped into a newly devised structural-symbolic representation that is independent of small view-angle changes. Large view-angle independence is achieved by multiple models for the same object. Incomplete line drawings and noisy line drawings are detected and modified by the preprocessing module before being presented to the pattern matcher. The matching process is carried out by production rule inferencing. The system implemented has the characteristics of being flexible and easy to modify. These advantages are rooted in the utilization of the rule-based approach. A test of the system has been carried out, with a satisfactory result.