The rough sets utilization for linguistic pattern recognition (Q1085989)

The paper approaches the nature of feature recognition process through description of image features in terms of the rough sets. Since the basic condition on image representation process must be satisfied by any recognition result, elementary features are defined as equivalence classes of possible occurrences of specific fragments existing in images. The names of equivalence classes (defined through specific numbers of objects and numbers of background parts covered by a window) constitute the lower linguistic approximation of window contents (i.e. names of recognized features). The upper linguistic approximation is formed by the lower linguistic approximation, its attributes and their parameters, all referred to object fragments situated in the window. The linguistic pattern recognition method presented, is resistant to change in thickness of contours or lines tracked by the window. The rough sets are utilized also on the level of image processing for noiseless image quantization.