распознавание образов а) в обработке изображений - анализ, описание, идентификация и классификация образов и других значимых сущностей с помощью компьютерных технологий. Используется, например, в биометрических методах контроля доступа для распознавания голоса, отпечатков пальцев, фотографий и т. п. Смотри также: APRP, centerline tracing, image processing, image recognition б) один из трёх базовых методов распознавания символов. В нём определяются правила, по которым строятся символы. Образ входного символа сравнивается с этим набором правил. Наибольшее соответствие тому или иному правилу определяет выбор выходного символа ASCII. Метод плохо работает с разорванными и перекрывающимися буквами Смотри также: character recognition, OCR
распознавание образов
A branch of artificial intelligence concerned with the classification or description of observations. Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set and the resulting learning strategy is characterised as supervised. Learning can also be unsupervised, in the sense that the system is not given an a priori labelling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns. The classification or description scheme usually uses one of the following approaches: statistical (or decision theoretic), syntactic (or structural), or neural. Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a probabilistic system. Structural pattern recognition is based on the structural interrelationships of features. Neural pattern recognition employs the neural computing paradigm that has emerged with neural networks.