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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 4 • Date July 1981

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Displaying Results 1 - 20 of 20
  • [Front cover]

    Page(s): c1
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  • List of Contributors

    Page(s): nil1
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  • [Breaker page]

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  • Representing and Processing Grammatical Modifiers

    Page(s): 357 - 367
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    Representing English adjectives and adverbs in a logically perspicuous notation (extended semantic networks [17]) and their computational accommodation for natural language comprehension within a state-based paradigm [4] are discussed herewith. Where appropriate, explicit comparisons are made between this representational method and other related approaches such as those in [2], [9], [11], [12], [15], [16], [18]. View full abstract»

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  • Shape Segmentation Using Relaxation

    Page(s): 368 - 375
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    Relaxation is applied to the segmentation of closed boundary curves of shapes. The ambiguous segmentation of the boundary is represented by a directed graph structure whose nodes represent segments, where two nodes are joined by an arc if the segments are consecutive along the boundary. A probability vector is associated with each node; each component of this vector provides an estimate of the probability that the corresponding segment is a particular part of the object. Relaxation is used to eliminate impossible sequences of parts, or reduce the probabilities of unlikely ones. In experiments involving airplane shapes, this almost always results in a drastic simplification of the graph with only good interpretations surviving. The approach is also extended to include curve linking and gap filling. A chain coded input image is broken into segments based on a measure of local curvature. Gap completions linking pairs of segments are then proposed and represented in a graph structure. A second graph, whose nodes consist of paths in the above graph, is constructed, and the nodes of the second graph are probabilistically classified as various object parts. Relaxation is then applied to increase the probability of mutually supporting classifications, and decrease the probability of unsupported decisions. A modified relaxation process using information about the size, spatial position, and orientation of the object parts yielded a high degree of disambiguation. View full abstract»

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  • Steps Toward Knowledge-Based Machine Translation

    Page(s): 376 - 392
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    This paper considers the possibilities for knowledge-based automatic text translation in the light of recent advances in artificial intelligence. It is argued that competent translation requires some reasonable depth of understanding of the source text, and, in particular, access to detailed contextual information. The following machine translation paradigm is proposed. First, the source text is analyzed and mapped into a language-free conceptual representation. Inference mechanisms then apply contextual world knowledge to augment the representation in various ways, adding information about items that were only implicit in the input text. Finally, a natural-language generator maps appropriate sections of the language-free representation into the target language. We discuss several difficult translation problems from this viewpoint with examples of English-to-Spanish and English-to-Russian translations; and illustrate possible solutions as embodied in a computer understander called SAM, which reads certain kinds of newspaper stories, then summarizes or paraphrases them in a variety of languages. View full abstract»

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  • A Comparison of the Stability Characteristics of Some Graph Theoretic Clustering Methods

    Page(s): 393 - 402
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    Assessing the stability of a clustering method involves the measurement of the extent to which the generated clusters are affected by perturbations in the input data. A measure which specifies the disturbance in a set of clusters as the minimum number of operations required to restore the set of modified clusters to the original ones is adopted. A number of well-known graph theoretic clustering methods are compared in terms of their stability as determined by this measure. Specifically, it is shown that among the clustering methods in any of several families of graph theoretic methods, clusters defined as the connected components are the most stable and the clusters specified as the maximal complete subgraphs are the least stable. Furthermore, as one proceeds from the method producing the most narrow clusters (maximal complete subgraphs) to those producing relatively broader clusters, the clustering process is shown to remain at least as stable as any method in the previous stages. Finally, the lower and the upper bounds for the measure of stability, when clusters are defined as the connected components, are derived. View full abstract»

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  • A Locally Adaptive Peano Scanning Algorithm

    Page(s): 403 - 412
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    This paper describes an algorithm which builds a ``Peano scanning,'' i.e., the reciprocal mapping, from [0, 1]n to [0, 1], of the well-known ``Peano curve.'' This Peano scanning is applied to a set of points in [0, 1]n and gives a one-dimensional image of it. Several applications of this technique have already been developed and are presented in this paper. View full abstract»

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  • Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach

    Page(s): 412 - 424
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    We approach the problem of labeling a set of objects from a quantitative standpoint. We define a world model in terms of transition probabilities and propose a definition of a class of global criteria that combine both ambiguity and consistency. A projected gradient algorithm is developed to minimize the criterion. We show that the minimization procedure can be implemented in a highly parallel manner. Results are shown on several examples and comparisons are made with relaxation labeling techniques. View full abstract»

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  • On Generalized Distance Transformation of Digitized Pictures

    Page(s): 424 - 443
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    In this paper, we present a generalization of the distance transformation of a digitized picture in two different aspects. First, we define the generalized distance transformation of a binary picture (GDTB). A subclass of GDTB, called a local minimum filter family of GDTB (LMF-GDTB), characterized by a series of local minimum filters with varying neighborhoods, is discussed in detail. A skeleton is defined for LMF-GDTB, and it is proved that any binary picture can be reconstructed exactly from its skeleton with the distance value on it. Second, the gray weighted distance transformation (GWDT) is extended to a generalized GWDT (GGWDT) by introducing an arbitrary initial picture. After the fundamental equation of GGWDT and its solution are derived, it is proved that an arbitrary gray picture is generated by iterative application of GGWDT from a uniquely determined elementary picture and a sequence of initial value pictures. View full abstract»

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  • Some Mathematical and Representational Aspects of Solid Modeling

    Page(s): 444 - 453
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    An approach is presented to the tripartite problem of modeling physical solids mathematically, representing the models in a computer, and using representations in geometric algorithms. Examples are primarily from the domain of manufacturing and design of discrete goods, but the results reported here have wider significance. Mathematical definitions can formalize many of our intuitions about three-dimensional (3-D) objects and operations on them. Representation-free (mathematical) models and functions allow formal properties to be defined for characterizing geometric representations. Three common representation schemes for 3-D objects are described briefly, along with some of their formal and informal properties. A rigorous, as opposed to ad hoc, approach to modeling has several advantages. Broadly, the conceptual complications and ambiguities which are endemic to ad hoc problem statements and solutions may be avoided by appealing to a precise mathematical semantics. Mathematical rigor is mandatory in applications such as automatic manufacturing in which correctness must be guaranteed and consistency and validity maintained. View full abstract»

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  • On the Influence of Sample Set Structure on Decision Rule Quality for the Case of a Linear Discriminant Function

    Page(s): 454 - 459
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    The influence of sample set structure on decision rule quality for the case of a linear discriminant function is considered. Specifically, the case of missing data in the sample set and the case when the multivariate random variable is to be registered with the help of a single-channel device are investigated. Some rather unusual phenomena are discussed, such as when some new samples are added to the sample set, and as a result the quality of parameter estimations used in a decision rule become better, but at the same time the quality of the decision rule itself becomes worse. The investigation is performed for the classical model of a twocategory classifier when the categories are described by the multivariate normal densities having common covariance matrices. Some results of statistical simulation experiments are included. View full abstract»

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  • Relaxation: Evaluation and Applications

    Page(s): 459 - 469
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    Probabilistic relaxation labeling processes are iterative parallel schemes that use contextual information to reduce local ambiguities. The behavior of these processes can be described by examining the rates of change and entropies of the probability vectors at each iteration. Examples are given comparing three relaxation processes as applied to several basic image analysis tasks. View full abstract»

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  • ANDAL: A Nonparametric Discrimination And Learning Algorithm for Recognition in Imperfectly Supervised Environments

    Page(s): 469 - 476
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    The problem of recognition in nonparametric environments under imperfect supervision is not amenable to solution through classical statistical approaches based on identification of finite mixtures, which require an a priori knowledge of the probabilistic descriptions of the classes. Accordingly, the problem is viewed in this study as one of optimal linear/nonlinear partitioning of the imperfectly labeled training sample set. This optimal partitioning is accomplished by defining an appropriate optimality criterion, which takes into account the imperfectness of supervision, and solving the resultant optimization problem through the Improved Flexible Polyhedron Method (IFPM). Possible alternatives to compensate for the inherent bias in this criterion towards equipopulation clusters are developed and evaluated using an illustrative example. Details of the methodology involved in implementing the approach are presented. Results of simulation experiments, which confirm the validity and effectiveness of this new technique in accomplishing optimal, linear/nonlinear discriminant learning in imperfectly supervised, nonparametric environments, are included. View full abstract»

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  • Some Notes on Cellular Logic Operators

    Page(s): 476 - 481
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    Cellular logic machines used for feature extraction in pattern recognition have increased in speed to the point of making it possible to execute programs equivalent to 1 billion general-purpose computer instructions in 1 TV frame time. Unfortunately, most cellular logic operators (CLO's) are designed ad hoc. It is important, therefore, to begin to systematize the generation of algorithms using CLO sequences for pattern analysis. These notes systematically analyze some aspects of CLO's which are used in shape discrimination and idealization and in object counting and sizing. New extensions of subfield numbering schemes in the hexagonal tessellation are introduced. View full abstract»

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  • Multidimensional Edge Detection by Hypersurface Fitting

    Page(s): 482 - 486
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    One way to define operators for detecting edges in digital images is to fit a surface (plane, quadric,...) to a neighborhood of each image point and take the magnitude of the gradient of the surface as an estimate of the rate of change of gray level in the image at that point. This approach is extended to define edge detectors applicable to multidimensional arrays of data-e.g., three-dimensional arrays obtained by reconstruction from projections-by locally fitting hypersurfaces to the data. The resulting operators, for hypersurfaces of degree 1 or 2, are closely analogous to those in the two-dimensional case. Examples comparing some of these three-dimensional operators with their twodimensional counterparts are given. View full abstract»

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  • [Advertisement]

    Page(s): 487
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  • [Advertisement]

    Page(s): 488
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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.

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Meet Our Editors

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