Showing papers in "Pattern Recognition in 1985"

TL;DR: By combining a nonparametric classifier, based on a clustering algorithm, with a quad-tree representation of the image, the scheme is both simple to implement and performs well, giving satisfactory results at signal-to-noise ratios well below 1.

TL;DR: The new discriminant analysis with orthonormal coordinate axes of the feature space is proposed, which is more powerful than the traditional one in so far as the discriminatory power and the mean error probability for coordinate axes are concerned.

TL;DR: This work has led to the development of a iterative fuzzy clustering technique which represents an image segmentation scheme which can be used as a preprocessor for a multivalued logic based computer vision system.

TL;DR: A graph theoretic scheme to link overlapping clusters is proposed in terms of the maximal complete subgraph (MCS) of a threshold graph, which is defined for a set of overlapping clusters by a given threshold value.

TL;DR: The paper reviews salient issues in the application of conventional techniques for extraction of information and the systems that use the artificial intelligence approach are characterized with respect to three key properties.

TL;DR: The rectangular influence graph is presented as an extension of the relative neighborhood graph (RNG) and it is shown that the RIG is a superset of the Gabriel graph with respect to any Minkowski metric.

TL;DR: This paper describes a method for detecting persons walking in a passageway from two consecutive TV images at long intervals by extracting candidate areas for persons from TV images by image processing.

TL;DR: The system of Hough technique includes envelope generation, thresholding, Hough transformation, and parameter determination, and thinning algorithms used for the skeletonization of the seismic reflectors in the seismogram.

TL;DR: Two segmentation methods for segmenting stacked seismic data into zones of common signal character based on texture analysis are described and their performance is demonstrated on a line of seismic data from the Gulf of Mexico that had been manually segmented.

TL;DR: Automatic identification of handprinted Hebrew characters is described in this paper, and the recognition model devised constitutes a multi-stage system that compared favorably with the results of two other recognition methods.

TL;DR: A synchronous parallel processing model is proposed which governs the communication and interaction between these tasks and presents a uniform framework for comparing and contrasting different formulation strategies.

TL;DR: Five distance-based statistics which reflect the degree to which a set of d-dimensional points is random are compared to identify the most promising statistic for intensive study and to establish performance bounds for situations involving little prior information.

TL;DR: A broad-based program or framework for treating the interpretation of seismic data as a problem or a collection of many problems in pattern recognition to infer the information necessary to accurately interpret exploration seismic data, and to locate potentially commercial hydrocarbon reservoirs is suggested.

TL;DR: Methods of estimating the orientation of a planar surface from the shapes of the contours of constant brightness on the surface, using perspective projection using two reflectance models are described.

TL;DR: This paper investigates the performance of the univariate sample linear discriminant function when the parameters are estimated under a model which takes the correlation into consideration.

TL;DR: A system which can recognize speech and generate the corresponding animation-like sign language sequence in real-time in a popular personal computer will help communication between deaf-mute and healthy persons.

TL;DR: Analysis of the results suggests that one-bit pressure patterns are usable in a sequential handwriter recognition scheme for first stage screening of incoming signatures.

TL;DR: An O ( n log n ) algorithm for computing the diameter of the set of lines is presented and it is shown that the algorithm is optimal to within a constant factor under the algebraic computation tree model of Ben-Or.

TL;DR: This paper presents parallel algorithms to determine properties of regions,such as area; to perform operations on regions, such as union and intersection; to determine if a point lies inside a region; and to determine whether a given digital curve could be the boundary of a region.

TL;DR: A system organization centered around a class of interconnection networks and a global bus is proposed for realizing the intertask communication requirements typically encountered in the parallel formulation of problems for image analysis.

TL;DR: A technique incorporating a rotating neighbourhood is introduced and utilized for image enhancement and texture derivation and a threshold which adapts itself to an unknown noise level and image segmentation into individual objects is presented.

TL;DR: An algorithm that runs in O ( n log n ) time for constructing the relative neighborhood graph based on the Delaunay triangulation is presented, improving a previously known algorithm that run in O 2 log n time.

TL;DR: In this paper, image segmentation and object recognition using syntactic methods are investigated and the segmentation process is embedded in the parsing algorithm.

TL;DR: It is shown that a linear oct-tree can be mapped into its three-dimensional border-representation by the repeated elimination of the internal boundaries existing between any two nodes by a recursive algorithm in a single pass through the input data.

TL;DR: In simulation experiments with normal, uniform and discrete distributions the smoothed additive estimators with fixed and variable smoothing parameter are compared to the leaving-one out method and the resubstitution method with respect to bias and variance.

TL;DR: An algorithm for the classification of data, based on what is called the self-learning approach, which has been applied to the recognition of the components of a mixture of normal distributions.

TL;DR: It is shown that by extracting event portions of the transient waveform through segmentation, a low order autoregressive model can provide an effective feature set for cluster analysis and event classification.

TL;DR: The results indicate that topographic labelings for images of some mathematically generated surfaces with varied directions of illumination do exist and will be useful in determining three-dimensional shape from two-dimensional images.

TL;DR: The calculation between error probability and Levenshtein distance is proposed and the system for two-dimensional seismic analysis includes three kinds of string distance computation to test the continuity of a bright spot pattern.

TL;DR: GYPSY analyzes images of objects on the basis of their two-dimensional shape to provide visual inspection and recognition in industrial automation tasks and is fully portable and easily programmable.