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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 3 • Date May 1981

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Displaying Results 1 - 24 of 24
  • [Front cover]

    Page(s): c1
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  • List of Contributors

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  • [Breaker page]

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  • Algorithms for Detecting M-Dimensional Objects in N-Dimensional Spaces

    Page(s): 245 - 256
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    Exact and approximate algorithms for detecting lines in a two-dimensional image space are discussed. For the case of uniformly distributed noise within an image space, transform methods and different notions of probability measures governing the parameters of the transforms are described. It is shown that different quantization schemes of the transformed space are desirable for different probabilistic assumptions. The quantization schemes are evaluated and compared. For one of the procedures that uses a generalized Duda-Hart procedure and a mixed quantization scheme, the time complexity to find all m-flats in n-space is shown to be bounded by O(ptm(n-m)2), where p is the number of points and t is a user parameter. For this procedure more true flats in a given orientation have been found and the number of spurious flats is small. View full abstract»

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  • A Pyramidal Representation of Images and Its Feature Extraction Facility

    Page(s): 257 - 264
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    This paper concerns a pyramidal representation of images. The objective of this study is to represent an image with as small a number of data words as possible in a memory without distorting the feature extraction facility inherent in pyramidal representations. First, a novel scheme for simplifying pyramidal representations is presented. Simplification is essentially based on the reduction of the number of informative elements observed in a window space during the hierarchical process of image representation. Following a brief explanation of the window pattern simplification process is a description of an overall procedure for simplifying complex binary images. Finally, for images which are not necessarily binary, procedure for rapid access to and selective use of particular areas and local control of detailed fineness are discussed in conjunction with the architectural considerations for implementing the system. View full abstract»

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  • Hierarchical Constraint Processes for Shape Analysis

    Page(s): 265 - 277
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    A major application of syntactic pattern recognition is the analysis of two-dimensional shape. This paper describes a new syntactic shape analysis technique which combines the constraint propagation techniques which have been so successful in computer vision with the syntactic representation techniques which have been successfully applied to a wide variety of shape analysis problems. Shapes are modeled by stratified shape grammars. These grammars are designed so that local constraints can be compiled from the grammar describing the appearance of pieces of shape at various levels of description. Applications to the analysis of airplane shapes are presented. View full abstract»

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  • A Differential Operator Technique for Restoring Degraded Signals and Images

    Page(s): 278 - 284
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    A technique is described for restoring signals, images, and other physical quantities that have been distorted or degraded by an imperfect measurement system. This technique is based upon the application of a specific differential operator to the measured quantity. For digital implementation, its advantages compared to other restoration techniques are simplicity, computational efficiency, and reduced core memory requirements. Calculations for a one-dimensional example indicate that restorations comparable in quality to Wiener-filter restorations are obtained with better than an order of magnitude decrease in computation time. View full abstract»

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  • Derivatives of Tree Sets with Applications to Grammatical Inference

    Page(s): 285 - 293
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    Tree automata generalize the notion of a finite automaton working on strings to that of a finite automaton operating on trees. Most results for finite automata have been extended to tree automata. In this paper we introduce tree derivatives which extend the concept of Brzozowski's string derivatives. We use tree derivatives for minimizing and characterizing tree automata. Tree derivatives are used as a basis of inference of tree automata from finite samples of trees. Our method compares tree derivative sets and infers a tree automaton based on the amount of overlap between the derivative sets. Several of the limitations present in the tree inference techniques by Brayer and Fu and Edwards, Gonzalez, and Thomason are not imposed by our algorithm. View full abstract»

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  • Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

    Page(s): 293 - 298
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    The minimum cost classifier when general cost functions are associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimization of the binary tree in this context is carried out using dynamic programming. This technique is applied to the voiced-unvoiced-silence classification in speech processing. View full abstract»

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  • Image Approximation by Variable Knot Bicubic Splines

    Page(s): 299 - 310
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    This paper presents a degree of freedom or information content analysis of images in the context of digital image processing. As such it represents an attempt to quantify the number of truly independent samples one gathers with imaging devices. The degrees of freedom of a sampled image itself are developed as an approximation problem. Here, bicubic splines with variable knots are employed in an attempt to answer the question as to what extent images are finitely representable in the context of digital sensors and computers. Relatively simple algorithms for good knot placement are given and result in spline approximations that achieve significant parameter reductions at acceptable error levels. The knots themselves are shown to be useful as an indicator of image activity and have potential as an image segmentation device, as well as easy implementation in CCD signal processing and focal plane smart sensor arrays. Both mathematical and experimental results are presented. View full abstract»

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  • Three-Dimensional Shape Analysis Using Local Shape Descriptors

    Page(s): 310 - 323
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    The three-dimensional shape analysis problem is a very demanding test of shape analysis algorithms. Previous approaches to the problem have employed global features such as moments and Fourier descriptors. Global features lack the capacity for solving the partial shape recognition problem, in which only part of the unknown shape is available. Previous approaches to local shape analysis have employed structural (syntactic) methods, but these methods have so far failed to solve the three-dimensional problem. This paper describes a hybrid structural/statistical local shape analysis algorithm which is applied to the three-dimensional problem. View full abstract»

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  • A Three-Dimensional Edge Operator

    Page(s): 324 - 331
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    Modern scanning techniques, such as computed tomography, have begun to produce true three-dimensional imagery of internal structures. The first stage in finding structure in these images, like that for standard two-dimensional images, is to evaluate a local edge operator over the image. If an edge segment in two dimensions is modeled as an oriented unit line segment that separates unit squares (i.e., pixels) of different intensities, then a three-dimensional edge segment is an oriented unit plane that separates unit volumes (i.e., voxels) of different intensities. In this correspondence we derive an operator that finds the best oriented plane at each point in the image. This operator, which is based directly on the 3-D problem, complements other approaches that are either interactive or heuristic extensions of 2-D techniques. View full abstract»

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  • Parallel Computation of Contour Properties

    Page(s): 331 - 337
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    Some contour properties can be derived in parallel by a string or cycle of automata in linear time, faster than can be done with a single processor. In particular, the intersection points of two contours, the straightness of a line, the union or intersection of two contours, and polygonal approximations of a contour are computed in linear time. View full abstract»

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  • Evaluation of Image Fidelity by Means of the Fidelogram and Level Mean-Square Error

    Page(s): 337 - 347
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    In the present correspondence a method for the representa-tion of image fidelity is proposed and demonstrated by experimental examples. A fidelity measure is also proposed, which is a formal exten-sion of the commonly utilized mean-square error. The characteristics of these fidelity representations are discussed. As a result, it is shown that the representations proposed here are effective, in the sense that they can display clearly the level variance and average levels of the processed image for the standard one, or make it possible to evaluate image fidelity from definite aspects, as compared to existing fidelity representations. View full abstract»

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  • Reliable Indexing Using Unreliable Recognition Devices

    Page(s): 347 - 350
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    A new method is described and tested for using an unreliable character recognition device to produce a reliable index for a collection of documents. All highly likely substitution errors of the recognition device are handled by transforming characters which confuse readily into the same pseudocharacter. An analysis of the method is done showing the expected precision (fraction of words correctly found to words present) and recall (fraction of words retrieved properly to those which were retrieved). Published substitution error matrices were employed, along with a large file of words and word frequencies to evaluate the method. Performance was surprisingly good. Suggestions for further enhancements are given. View full abstract»

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  • Interactive Pattern Recognition

    Page(s): 351
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  • Syntactic Pattern Recognition, An Introduction

    Page(s): 351 - 352
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  • Structural Pattern Recognition

    Page(s): 352
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  • Biomedical Pattern Recognition and Image Processing

    Page(s): 352
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  • List of Reviewers

    Page(s): 353 - 354
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  • [Advertisement]

    Page(s): 355
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  • [Advertisement]

    Page(s): 356
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  • List of Contributors

    Page(s): nil2
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  • [Front cover]

    Page(s): c2
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Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
David A. Forsyth
University of Illinois