Showing papers in "Pattern Recognition in 1971"

TL;DR: A spectrum of possibilities has been demonstrated, placing several apparently conflicting recent results in perspective.

TL;DR: An algorithm to perceive color is introduced and its application to visual perception is demonstrated and the number of objects in the scene and the color, shape, size and position of each object are obtained.

TL;DR: The design of the FIDAC instrument is described, from original basic requirements to ultimate physical realization, and the problems of achieving dynamic focus, light uniformity, and positional accuracy are discussed.

TL;DR: This paper describes some exploratory work undertaken to study operationally viable structures for syntax-aided learning in the context of handprinted letter recognition, and argues for the need to distinguish between syntax-controlled and syntax- aided recognition schemes.

TL;DR: A two dimensional language that produces line pictures of polygons that may be decomposed into 45° right triangles and rectangles and an example illustrates use of the language to produce a picture.

TL;DR: Three main classes of formal description are surveyed and evaluated: the grammar-based approach, which uses a set of rules to describe the arrangement and relationships among the picture primitives; the descriptor-based Approach, which captures the content of the picture by using a number of terms or phrases; and the procedure- based approach, in which a system is coupled to a high-level control mechanism.

TL;DR: Two examples of structure description are introduced, one of pictorial patterns with a set of group-theoretical rules and the other aSet of rules for linearization of chemical structural formulae, both of which are grammatical in the sense that they are based on production rules.

TL;DR: Several extensions of standard linguistic recognizers are described, to give flexibility in handling loosely connected, noisy, messy patterns.

TL;DR: A hypothesis has been suggested which might explain the importance of simplicity of description in pattern recognition and in scientific theories.

TL;DR: Random superimposed coding has reduced the massive storage requirements of the Bledsoe and Browning Method of Pattern Recognition, applied to unconstrained hand-printed numerals with n = 14, by a factor of roughly four.

TL;DR: This paper examines the question of how to make a determination of success for discriminant analysis given that the basis for such a judgment lies in estimates of expected error rates, and compares several techniques in a setting of high true probabilities of misclassification.

TL;DR: Methods for extracting pattern-synthesizing features based on matrix factorization and pattern intersection are presented and an algorithm for obtaining features correlated by inclusion is discussed.

TL;DR: An interactive graphics system which is being used in the solution of both pattern recognition and pattern analysis problems and its relationship to the larger On-Line Pattern Analysis and Recognition System (OLPARS) is discussed.

TL;DR: An approach to interactive pattern recognition is described which provides for using a priori problem knowledge in the form of uncertain correlation information among features or new features which are nonlinear functions of original features.

TL;DR: This paper discusses the evaluation of feature quality or “goodness” by means of a similarity functional, and an experimental method for estimating feature goodness is specified.

TL;DR: A piecewise-linear pattern classifier is applied to the interpretation of low resolution mass spectral data and results are presented for classification problems pertaining to molecular formula and structural information and are compared with results from a binary pattern classifiers.

TL;DR: Electronic image devices' behavior when used specifically for pattern recognition preprocessing and the performance of the image dissector and the plumbicon tube in a pseudo-random scanning system are discussed.

TL;DR: A local interpolation method, fit to automatic computation, is proposed, which gives the upper limit of error, and restores the surface by giving it the same order of error as that of the digitized sample error.

TL;DR: It is argued that the nature of the representation produced by the first layer determines the amount and quality of subsequent processing.

TL;DR: P measures of association and similarity are combined to perform a cluster analysis of variables concerned with behavioral problems of deaf children, and the four major clusters which emerge are identified as characteristics of anxiety, hot temperedness, inattentiveness, and social withdrawal.

TL;DR: A precision mechanical scanner has been constructed for the purpose of preparing digital transcriptions of micrographs for computer analysis, and of performing elementary analysis without resort to a computer.

TL;DR: Some expressions describing the quantitative effects on processed images of different types of frequency-domain errors are presented so that an appreciation of the subjective effects of the errors on image quality may be obtained.

TL;DR: Duality concepts in pattern recognition research are described, and Measurements are considered to form the coordinate axes of the primal space, and sample patterns are consider to provide the coordinate axis of the dual space.

TL;DR: The error rate obtained for a simple enhancement algorithm is examined using the criterion developed in this model, which relates the effective system channel capacity to the number of sub-areas of each sensor to be estimated, and to the error rate involved in making these estimates.

TL;DR: A method is presented to synthesize threshold element networks by iterative computation of the parameters by iteratively modifying the parameters under the control of a gain function.

TL;DR: Methods of Estimation Theory are used to show that reinforcement learning is implemented by sequential parameter estimation which alters both a priori and spontaneously learned templates feature by feature.

TL;DR: The theoretical foundation of the design of a learning multiclass pattern classifier based on adaptive threshold gates is included, and the conclusions and applicability of such a classifier are illustrated with a small adjustable multiclass classifier called AMOS.