Patent GB-1243969-A (GB)
Title: Improvements relating to pattern recognition devices
Inventor: INGHAM WILLIAM ELLIS
IPC: G06V10/75
Filed: 1967-11-15
Abstract: 1,243,969. Pattern recognition. E.M.I. Ltd. 6 Nov., 1968 [15 Nov., 1967], No. 52012/67. Heading G4R. In a pattern recognition device, one or more sensed pattern features are used to derive the most likely class for the pattern to belong to, a likely further feature, and the location of the latter, using stored reference data, the further feature being searched for at its specified location, any feature detected at this location being used to decide if the class selected remains the most likely class. A TV camera executes a search raster to locate the pattern, then follows its outline (or shape) with an advancing circle. The curvature of the outline is continually computed and when it changes significantly the curvature (and possibly position and/or curve length) is supplied to a recognition system as a feature, together with its position. A closed loop, e.g. spot, restarts the search raster. Learning mode.-Each feature sensed from a given pattern is compared in turn with each feature from a feature ("descriptor") store, the degree of fit in each case being stored in a temporary store with the name of the feature (this name being the address of the feature in the feature store). The name of the featnre with the highest degree of fit so far is stored in a feature name store if the degree of fit exceeds a threshold. If the degree of fit does not exceed the threshold, the sensed feature (i.e. from the pattern scanning) is stored in the feature store and its name is stored in the feature name store. The feature name store is cleared for each new pattern. The position signals accompanying each sensed feature resulting in a feature name being stored in the feature name store, are stored in a position list store (cleared for each new pattern). A distance computer computes and stores (for each class separately, the class name of the pattern being supplied by the operator) the average distance between each two features in the feature name store using the positions in the position list store, these average distances being updated as successive patterns are presented. A feature probability computer counts and stores (for each class separately) the number of times each particular feature name occurs in a given class. A class probability computer computes and stores the likelihoods (formula given) of classes for given features. A class name predictor develops and stores a confidence level for each class for the input pattern using the stored data in the class probability computer and the feature name store. The name of the class with the highest confidence level is compared with the class name supplied by the operator, and if the confidence level exceeds an acceptance threshold the class name is supplied to a predicted class output. The result of the comparison is used to adjust the acceptance thresholds or weight (unspecified) parameters. Recognition mode.-Each feature sensed is compared in turn with each feature from the feature store, the degree of fit and name being stored in the temporary store. The feature name associated with the highest degree of fit in the temporary store so far is stored in the feature name store. The confidence levels for the different classes, stored in the class name predictor, are updated using the feature added to the feature name store and the stored contents of the class probability computer, as during learning. The class name with the highest confidence level is used to select the feature name most likely to occur in this class (after any feature names already in the feature name store), using the stored data in the feature probability computer. When there are at least three feature names in the feature name store, a locality predictor predicts the position of the "next most likely to occur" feature just selected using triangulation on the last three positions stored in the position list store, the predicted position being in the form of displacement from the current position. A search is performed in the predicted position and any feature sensed is treated as before, the feature name giving the best fit to this feature being compared with the predicted feature name. Equality causes the "best-fit" feature name to be entered in the feature name store and used by the class name predictor as before. Inequality causes outline following of the pattern to be continued from the position reached before the unsuccessful prediction was made. When the class name with the highest confidence level (in the class name predictor) exceeds its acceptance threshold, it is gated to the. predicted class output to identify the pattern class. Modifications.-The system could -be implemented by programming a general-purpose digital computer. Instead of sensing the pattern direct, the system may use a representation stored in a magnetic core or drum store. The feature names may be used to predict group names which are used to predict class names. Crossings and junctions could be used as additional features. The operator may direct the scanning beam during learning to the inside edge of the pattern.