Abstract
A new computer based method for visual pattern recognition is described. The process extracts shape features from binary edge images represented in either real-valued or discrete coordinates. Both separate and overlapping features are extracted, maximizing their completeness and continuity, without using templates or mathematical feature descriptions. These features and their arrangement are encoded in a shift, rotation, and scale invariant manner and used to recognize incoming patterns from a set of previously encoded patterns stored in memory. New patterns may be automatically classified and stored in memory, allowing the system to learn and generalize without operator intervention. Testing shows the system to be completely shift, rotation, and scale invariant, and insensitive to local image distortion.
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