Showing papers in "Pattern Recognition in 1969"

TL;DR: An experiment on line extraction and pattern detection in a photograph is performed and certain portions of a face may be enough for the decision even if the other portion of the face is hidden in noise and could not be detected as such.

TL;DR: The contents of the papers are abstracted and summarized in six sections: Picture Digitization; Picture Preprocessing; Feature Extraction; Data Editing; Pattern Recognition; and Picture Data Measurement.

TL;DR: This nonparametric method, based on estimating the probability densities of two populations, is combined with Gamba's “learning without teacher” or classification procedure to obtain a new classification technique which considers all variates simultaneously.

TL;DR: Statistically weighted mask, multiple weighted masks, and n -tuple recognition methods were applied to typed numerals taken from envelopes in the mail to study the effects of substitution errors and reject errors.

TL;DR: This algorithm is applicable to nonautomatic welding, and it does not require any treatment for taking X-ray photographs, and is required only once for each kind of welding.

TL;DR: An adaptive pattern recognition program that learns to recognize coded line drawings, and to describe the structure of the pattern by giving the hierarchical organization of its subparts, and the spatial relationships between them is discussed.

TL;DR: A computer program for doing the learning of patterns in terms of previously learned patterns is described and the importance of its memory organization is discussed.

TL;DR: It is shown to be effective to select a small number of “representative” objects first and to apply the clustering program on them, and to place them in the generated classes by the pattern recognition technique.

TL;DR: An efficient computer method which uses an extension of the familiar gravitational field to find clusters of multidimensional data is suggested, and the manipulation of a single parameter permits one's perspective of the data to range from the locally sensitive to the globally sensitive.

TL;DR: The data extraction process for bubble chamber film is described from a pattern recognition viewpoint and major problem areas and semi-automatic measuring devices are discussed.