Feature classification using supervised statistical pattern recognition

摘要

Feature classification using a novel supervised statistical pattern recognition approach is described. A tree-like hierarchical decomposition of n-dimensional feature space is created off-line from an image processing system. The hierarchical tree is created through a minimax- type decompositional segregation of n-dimensional feature vectors of different feature classifications within the corresponding feature space. Each cell preferably contains feature vectors of only one feature classification, or is empty, or is of a predefined minimum cell size. Once created, the hierarchical tree is made available to the image processing system for real-time defect classification of features in a static or moving pattern. Each feature is indexed to the classification tree by locating its corresponding feature vector in the appropriate feature space cell as determined by a depth-first search of the hierarchical tree. The smallest leaf node which includes that feature vector provides the statistical information on the vector's classification.