It has been pointed out that any heuristic for problem solving - weak or strong - is based on the recognition of a unary relation (subset) or a relation of higher arity on the set of states of the problem. Since the r...
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It has been pointed out that any heuristic for problem solving - weak or strong - is based on the recognition of a unary relation (subset) or a relation of higher arity on the set of states of the problem. Since the recognition of such relations from examples (often obtained by gedanken experiments on the problem) is a necessary part of the development of such heuristics, one can effectively develop heuristics automatically by using patternrecognition techniques. This thesis is supported by simple examples of problems solved by heuristics developed by a computor program.
An efficient procedure which integrates feature selection and binary decision tree construction is presented. The nonparametric approach is based on the Kolmogorov-Smirnov criterion which yields an optimal classificat...
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An efficient procedure which integrates feature selection and binary decision tree construction is presented. The nonparametric approach is based on the Kolmogorov-Smirnov criterion which yields an optimal classification decision at each node. By combining the feature selection with the design of the classifier, only the most informative features are retained for classification.
A discussion is presented of a facet model for image data which has the potential for fitting the form of the real idealized image, and for describing how the observed image differs from the idealized form. It is also...
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A discussion is presented of a facet model for image data which has the potential for fitting the form of the real idealized image, and for describing how the observed image differs from the idealized form. It is also an appropriate form for a variety of imageprocessing algorithms. Then the authors give a relaxation procedure, and prove its convergence, for determining an estimate of the ideal image from observed image data.
Methodologies in statistical imageprocessing and recognition are considered. Specific areas considered are the following: (1) The decision rules in imagerecognition and their comparative evaluation under finite samp...
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Methodologies in statistical imageprocessing and recognition are considered. Specific areas considered are the following: (1) The decision rules in imagerecognition and their comparative evaluation under finite sample size condition; (2) Statistical feature extraction techniques for image segmentation with emphasis on the statistical characteristic of textural features; (3) Statistical contextual analysis algorithms for images. Emphasis is placed on the contextual preprocessing/postprocessing techniques to implement the optimum decision rules with context; (4) Statistical image modeling techniques including the nonhomogeneous models and the autoregressive models. The software problems involved in these areas also are examined in detail.
The focus of this study is to develop an understanding of the state of the art in visual motion perception by intelligent systems. The authors examine diverse theoretical and empirical approaches to visual motion anal...
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The focus of this study is to develop an understanding of the state of the art in visual motion perception by intelligent systems. The authors examine diverse theoretical and empirical approaches to visual motion analysis, perception, and understanding. Emphasis is placed on issues of observed object and image sequence description, representation, and perceptual control strategies. The author introduces two concepts in visual motion perception: motion vantage perspective and object motion coherence.
This study deals with an optimization technique applied to the problem of stochastic labeling. The authors propose a definition of a global criterion on a set of objects to be labeled that combines both ambiguity and ...
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This study deals with an optimization technique applied to the problem of stochastic labeling. The authors propose a definition of a global criterion on a set of objects to be labeled that combines both ambiguity and consistency with adjustable weights. A projected gradient algorithm is developed to minimize the criterion. Results are shown on a toy example and on the edge detection problem. Comparisons are made with relaxation labeling techniques.
A method for optimizing the performance of the line detector described by Duda and Hart is presented. Comparisons are made of the error probabilities for the optimal and previous schemes. A method for approximating th...
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A method for optimizing the performance of the line detector described by Duda and Hart is presented. Comparisons are made of the error probabilities for the optimal and previous schemes. A method for approximating the optimal scheme is also presented.
The potential uses of Charge Transfer Devices (CTDs) in pattern classification operations are explored. The needs for a hardware-based pattern classifier are established, and a matrix multiplication subsystem based up...
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The potential uses of Charge Transfer Devices (CTDs) in pattern classification operations are explored. The needs for a hardware-based pattern classifier are established, and a matrix multiplication subsystem based upon a sum-of-products CTD is presented. Applications of the subsystem to the classification of multi-modal Gaussian distributions in general and to LANDSAT data processing in particular are discussed. The potential impact of this technology on satellite data processing methodologies is discussed.
Let a vector of probabilities be associated with every node of a graph. These probabilities define a random variable representing the possible labels of the node. Probabilities at neighboring nodes are used iterativel...
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Let a vector of probabilities be associated with every node of a graph. These probabilities define a random variable representing the possible labels of the node. Probabilities at neighboring nodes are used iteratively to update the probabilities at a given node based on statistical relations among node labels. The results are compared with previous work on probabilistic relaxation labeling, and examples are given from the image segmentation domain. References are also given to applications of the new scheme in text processing.
Consideration is given to a class of patterns which are subject to the action of a group of transformations. The author is particularly concerned with the existence of measurements or features which are invariant with...
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Consideration is given to a class of patterns which are subject to the action of a group of transformations. The author is particularly concerned with the existence of measurements or features which are invariant with respect to transformation. A concept of relative invariance is also introduced and explored in depth. In a very general sense, it is shown that every invariant (and relative invariant) is a suitable average over the relevant group of transformations. Finally, invariant means of bounded functions are used to explore existence of pattern invariants. Suggestions for further research are also given.
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