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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 3 • Date May 1983

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Displaying Results 1 - 16 of 16
  • [Front cover]

    Page(s): c1
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  • List of Contributors

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  • [Breaker page]

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  • Contexts and Data Dependencies: A Synthesis

    Page(s): 237 - 246
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    Two data-organization devices that have come out of AI re-search are data pools (``contexts'') and data dependencies. The latter are more flexible than the former, and have supplanted them. Data pools offer certain advantages of efficiency, however, so it is worth trying to make the two mechanisms compatible. Doing this requires generalizing the mark-and-sweep algorithms that maintain consistency in a data-dependency network, so that the labels passed around do not simply say whether a datum is IN or OUT, but say which data pools it is present in. The revised algorithm is essentially an algorithm for solving simultaneous Boolean equations. Other mechanisms are needed for per-forming useful chores like maintaining well-founded support links and orchestrating demon calls. View full abstract»

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  • Planning in Time: Windows and Durations for Activities and Goals

    Page(s): 246 - 267
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    A general purpose automated planner/scheduler is described which generates parallel plans to achieve goals with imposed time con-straints. Both durations and start time windows may be specified for sets of goal conditions. The parallel plans consist of not just actions but also of events (triggered by circumstances), inferences, and scheduled events (completely beyond the actor's control). Deterministic dura-tions of all such activities are explicitly modeled, and may be any com-putable function of the activity variables. A start time window for each activity in the plan is updated dynamically during plan generation, in order to maintain consistency with the windows and durations of adja-cent activities and goals. The plans are tailored around scheduled events. The final plan network resembles a PERT chart. From this a schedule of nominal start times for each activity is generated. Ex-amples are drawn from the traditional blocksworld and also from a real-istic ``Spaceworld,'' in which an autonomous spacecraft photographs objects in deep space and transmits the information to Earth. The author is with the Information Systems Research Section, Jet Propulsion Laboratory, Pasadena, CA 91109. View full abstract»

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  • On the Foundations of Relaxation Labeling Processes

    Page(s): 267 - 287
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    A large class of problems can be formulated in terms of the assignment of labels to objects. Frequently, processes are needed which reduce ambiguity and noise, and select the best label among several possible choices. Relaxation labeling processes are just such a class of algorithms. They are based on the parallel use of local constraints between labels. This paper develops a theory to characterize the goal of relaxation labeling. The theory is founded on a definition of con-sistency in labelings, extending the notion of constraint satisfaction. In certain restricted circumstances, an explicit functional exists that can be maximized to guide the search for consistent labelings. This functional is used to derive a new relaxation labeling operator. When the restrictions are not satisfied, the theory relies on variational cal-culus. It is shown that the problem of finding consistent labelings is equivalent to solving a variational inequality. A procedure nearly identical to the relaxation operator derived under restricted circum-stances serves in the more general setting. Further, a local convergence result is established for this operator. The standard relaxation labeling formulas are shown to approximate our new operator, which leads us to conjecture that successful applications of the standard methods are explainable by the theory developed here. Observations about con-vergence and generalizations to higher order compatibility relations are described. View full abstract»

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  • Problem Reduction Representation for the Linguistic Analysis of Waveforms

    Page(s): 287 - 298
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    This paper shows how the nondirectional structural analysis of pattern data can be performed by matching a problem reduction representation (PRR) of pattern structure with sample data, using a best-first state space search algorithm called SSS*. The end result of the matching algorithm is a tree whose nodes represent recognized structures in the data. Tip nodes of the tree structure correspond to primitives which are recognized in the raw data by curve fitting routines. The operators of the algorithm allow the tree to be constructed with a combination of top-down or bottom-up steps. The matching of the structure tree to waveform segments need not be done in a left-right sequence. Moreover ambiguous matches are pursued in a best first order by using state space search with partial parse trees as states. A software system called WAPSYS (for waveform parsing system) is described, which implements this structural analysis paradigm. Experience using WAPSYS to analyze carotid pulse waves is also discussed. View full abstract»

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  • Multiple-Window Parallel Adaptive Boundary Finding in Computer Vision

    Page(s): 299 - 316
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    The problem considered in this paper is the estimation of highly variable object boundaries in noisy images. Boundaries may be those of a tank in an IR image, a spinal canal in a CAT scan, a cloud in a visible light image, etc. Or they may be internal to an object such as the boundary between a spherical surface and a cylindrical surface in a manufactured object. The focus of the paper is on parallel multiple-window boundary estimation algorithms. Here the image field is parti-tioned into an array of rectangular windows, and boundary finders are run simultaneously within the windows. The boundary segments found within the windows are then seamed together to obtain meaningful global boundaries. The entire procedure is treated within a maximum likelihood estimation framework that we have developed for boundary finding. Although our multiple-window estimation approach can be used with a number of local boundary finding algorithms, we concen-trate on one which is based on dynamic programming and will produce the true maximum likelihood boundary. Some theoretical considera-tions for boundary model design and boundary-finding runtime are covered. Included is the use of a low computational cost F-test for test-ing whether a window contains a boundary, and an analytical treatment which shows that use of coarse pixels with a chi-square test or an F-test improves the probability of correctly recognizing whether a boundary is present in a window. View full abstract»

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  • Applications of Vector Fields to Image Processing

    Page(s): 316 - 329
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    We use rotational and curvature properties of vector fields to identify critical features of an image. Using vector analysis and dif-ferential geometry, we establish the properties needed, and then use these properties in three ways. First, our results make it theoretically possible to identify extremal edges of an intensity function f(x, y) of two variables by considering the gradient vector field V = ¿f. There is also enough information in ¿f to find regions of high curvature (i.e., high curvature of the level paths of f). For color images, we use the vector field V = (I, Q). In application, the image is partitioned into a grid of squares. On the boundary of each square, V/|V| is sampled, and these unit vectors are used as the tangents of a curve ¿. The rotation number (or topological degree) ¿(¿) and the average curvature f|¿¿| are computed for each square. Analysis of these numbers yields infor-mation on edges and curvature. Experimental results from both simu-lated and real data are described. View full abstract»

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  • A Gradient Projection Algorithm for Relaxation Methods

    Page(s): 330 - 332
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    We consider a particular problem which arises when apply-ing the method of gradient projection for solving constrained optimiza-tion and finite dimensional variational inequalities on the convex set formed by the convex hull of the standard basis unit vectors. The method is especially important for relaxation labeling techniques applied to problems in artificial intelligence. Zoutendijk's method for finding feasible directions, which is relatively complicated in general situations, yields a very simple finite algorithm for this problem. We present an extremely simple algorithm for performing the gradient projection and an independent verification of its correctness. View full abstract»

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  • An Image Transform Coding Scheme Based on Spatial Domain Considerations

    Page(s): 332 - 337
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    Image transform coding is first briefly reviewed using conventional viewpoints. Then a new spatial domain interpretation is given to image transform coding. An improvement based on this viewpoint for the Fourier transform coding, which possesses simple spatial domain relations, is presented. View full abstract»

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  • Optimum Recursive Filtering of Noisy Two-Dimensional Data with Sequential Parameter Identification

    Page(s): 337 - 344
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    A two-dimensional recursive estimation algorithm based on the asymmetric half-plane model is described for the problem of MMSE (minimum mean-square error) filtering. The optimum filtering problem is solved by formulating the asymmetric half-plane ARMA (autoregressive moving average) model for two-dimensional data. The sequential parameter identification from the noisy two-dimensional data is also discussed, utilizing the stochastic approximation. Experiments were performed for real image data, combining the proposed parameter identification and estimation algorithms. The results show that this method gives considerable improvement in SNR. View full abstract»

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  • Using Pyramids to Define Local Thresholds for Blob Detection

    Page(s): 345 - 349
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    A method of detecting blobs in images is described. The method involves building a succession of lower resolution images and looking for spots in these images. A spot in a low resolution image corresponds to a distinguished compact region in a known position in the original image. Further, it is possible to calculate thresholds in the low resolution image, using very simple methods, and to apply those thresholds to the region of the original image corresponding to the spot. Examples are shown in which variations of the technique are applied to several images. View full abstract»

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  • An Iterative Approach to Region Growing Using Associative Memories

    Page(s): 349 - 352
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    The postulate is made that ``any computation which can be performed recursively can be performed easily and efficiently by iteration coupled with association.'' The ``easily and efficiently'' part of that postulate is nontrivial to prove, and is shown by examples in this paper. The use of association leads directly to potential implementation by content-addressable memories. The example addressed is region growing, often given as a classical example of the use of recursive control structures in image processing. Recursive control structures, however, are somewhat awkward to build in hardware, where the intent is to segment an image at raster scan rates. This paper describes an algorithm and hardware structure capable of per-forming region labeling iteratively at scan rates. Every pixel is individually labeled with an identifier signifying to which region it belongs. The difficulties which often justify recursion (``U''- and ``N''-shaped regions, etc.) are handled by maintaining an equivalence table in hardware, transparent to the computer, which reads the labeled pixels. The mechanism for updating the region map is explained in detail. Furthermore, simulation of the associative memory has been demon-strated to be an effective implementation of region growing in a serial computer. View full abstract»

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  • List of Contributors

    Page(s): nil2
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    Freely Available from IEEE
  • [Front cover]

    Page(s): c2
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    Freely Available from IEEE

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