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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 5 • Date Sept. 1982

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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]

    Page(s): nil1
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  • Opinion

    Page(s): 457
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  • Mathematical Structures of Line Drawings of Polyhedrons-Toward Man-Machine Communication by Means of Line Drawings

    Page(s): 458 - 469
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    Mathematical structures of line drawings of polyhedrons are studied and practical as well as theoretical solutions are obtained for several fundamental problems aroused in scene analysis and in man-machine communication. First, a necessary and sufficient condition for a line drawing to correctly represent a polyhedron is obtained in terms of linear algebra. Next, combinatorial structures are investigated and practical solutions are obtained to such problems as how to discriminate between correct and incorrect line drawings and how to correct vertex-position errors in incorrect line drawings. Lastly, distribution of the degree of freedom of a line drawing is elucidated and a method is proposed for interactive reconstruction of a polyhedron from a line drawing. The results obtained here enable us to make manmachine communication more ``flexible'' in the sense that a machine can reconstruct three-dimensional objects from hand-drawn pictures even if the pictures are not perfect. View full abstract»

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  • Identification of Space Curves from Two-Dimensional Perspective Views

    Page(s): 469 - 475
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    This paper describes a new method to be used for matching three-dimensional objects with curved surfaces to two-dimensional perspective views. The method requires for each three-dimensional object a stored model consisting of a closed space curve representing some characteristic connected curved edges of the object. The input is a two-dimensional perspective projection of one of the stored models represented by an ordered sequence of points. The input is converted to a spline representation which is sampled at equal intervals to derive a curvature function. The Fourier transform of the curvature function is used to represent the shape. The actual matching is reduced to a minimization problem which is handled by the Levenberg-Marquardt algorithm [3]. View full abstract»

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  • Locating Structures in Aerial Images

    Page(s): 476 - 484
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    A technique for locating desired structures utilizing user specified information about properties of these structures and their relationships with other more easily extracted objects is described. An edge-based and region-based technique is used for scene segmentation. Experimental results of the processing of aerial pictures are presented. View full abstract»

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  • Automata in Random Environments with Application to Machine Intelligence

    Page(s): 485 - 492
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    Computers and brains are modeled by finite and probabilistic automata, respectively. Probabilistic automata are known to be strictly more powerful than finite automata. The observation that the environment affects behavior of both computer and brain is made. Automata are then modeled in an environment. Theorem 1 shows that useful environmental models are those which are infinite sets. A probabilistic structure is placed on the environment set. Theorem 2 compares the behavior of finite (deterministic) and probabilistic automata in random environments. Several interpretations of Theorem 2 are discussed which offer some insight into some mathematical limits of machine intelligence. View full abstract»

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  • Some Graphical Considerations in Time Series Analysis

    Page(s): 493 - 499
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    The pictorial information in a stationary time series as depicted by crossings of levels and crossings of random levels and related quantities is studied. It is shown that such graphical features are directly connected with the covariance function and hence with the spectral density. Many of these features can be actually applied in estimation and in the study of extremes. In the Gaussian case, the finite dimensional distributions are completely determined by the axis crossings and by the crossings of a random curve (to be defined) if the process is essentially bounded. Certain graphical patterns are suggested for a fast recognition of low-order ARMA models. View full abstract»

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  • Region Extraction from Complex Shapes

    Page(s): 500 - 511
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    An algorithm is described which extracts primitive regions (i.e., convex, spiral shaped, and biconcave lens) from complex shapes. The interior region bounded by the shape is decomposed by first slicing it into a set of convex subregions and then rotating and dissolving the various boundaries between subregions until a satisfactory decomposition is obtained. The same algorithm also is used to decompose the exterior region between the shape and its convex hull. The algorithm has been implemented as an Algol-W computer program for the UNIVAC 90/80 and results of running the program are presented for a wide variety of complex shapes. These results compare favorably with the experience reported by previous programs. View full abstract»

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  • Efficient Calculation of Primary Images from a Set of Images

    Page(s): 511 - 515
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    A set of images is modeled as a stochastic process and Karhunen-Loeve expansion is applied to extract the feature images. Although the size of the correlation matrix for such a stochastic process is very large, we show the way to calculate the eigenvectors when the rank of the correlation matrix is not large. We also propose an iterative algorithm to calculate the eigenvectors which save computation time andc omputer storage requirements. This iterative algorithm gains its efficiency from the fact that only a significant set of eigenvectors are retained at any stage of iteration. Simulation results are also presented to verify these methods. View full abstract»

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  • Moving Target Tracking Using Symbolic Registration

    Page(s): 515 - 520
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    This correspondence describes a moving target tracking (MTT) algorithm that performs image registration and motion analysis between pairs of images from a passive sensor. Unlike previously reported moving target indicators that operate at the signal level, the registration and motion analysis in the MTT is totally performed at a symbolic level. The operation of the MTT is demonstrated by simulation results obtained from applications of the algorithm to infrared images. View full abstract»

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  • Experiments in Text Recognition with Binary n-Gram and Viterbi Algorithms

    Page(s): 520 - 530
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    The binary n-gram and Viterbi algorithms have been suggested as alternative approaches to contextual postprocessing for text produced by a noisy channel such as an optical character recognizer. This correspondence describes the underlying theory of each approach in unified terminology, and presents new implementation algorithms for each approach. In particular, a storage efficient data structure is proposed for the binary n-gram algorithm and a recursive formulation is given for the Viterbi algorithm. Results of extensive experiments with each algorithm are described. View full abstract»

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  • The Use of Shrinkage Estimators in Linear Discriminant Analysis

    Page(s): 530 - 537
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    Probably the most common single discriminant algorithm in use today is the linear algorithm. Unfortunately, this algorithm has been shown to frequently behave poorly in high dimensions relative to other algorithms, even on suitable Gaussian data. This is because the algorithm uses sample estimates of the means and covariance matrix which are of poor quality in high dimensions. It seems reasonable that if these unbiased estimates were replaced by estimates which are more stable in high dimensions, then the resultant modified linear algorithm should be an improvement. This paper studies using a shrinkage estimate for the covariance matrix in the linear algorithm. We chose the linear algorithm, not because we particularly advocate its use, but because its simple structure allows one to more easily ascertain the effects of the use of shrinkage estimates. A simulation study assuming two underlying Gaussian populations with common covariance matrix found the shrinkage algorithm to significantly outperform the standard linear algorithm in most cases. Several different means, covariance matrices, and shrinkage rules were studied. A nonparametric algorithm, which previously had been shown to usually outperform the linear algorithm in high dimensions, was included in the simulation study for comparison. View full abstract»

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  • A Dimensionality Reduction Technique Based on a Least Squared Error Criterion

    Page(s): 537 - 544
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    A method of achieving dimensionality reduction is presented. The reduced dimensionality is achieved by utilizing a least squared error technique under the assumption that the goodness criterion is the maximum separation of classes. The criterion is met by first maximizing the spread of the cluster centers, and then minimizing the within class scatter. The derivation of the desired transformation from an arbitrary p-space to a space of lower dimension, say l, is completed with the assumption that the cluster centers are known. The criterion for the cluster center location is the minimization of the variance of the distance between the cluster center and the transformed pattern. It is demonstrated that the resulting cluster center set is similar to the simplex signal set in communication theory, which is a minimum energy signal set. View full abstract»

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  • Discrete Optimization by Relational Constraint Satisfaction

    Page(s): 544 - 551
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    In pattern matching a basic problem is to determine one or more vectors X that maximize an objective function which is a sum of functions of components of X. When this problem is solved by dynamic programming CPU time and storage requirements grow explosively as the amount of intervariable interaction in the objective function increases. This explosion may be reduced by departing from the traditional dynamic programming method of eliminating successive variables and instead determining a constraint relation between each variable and all others with which it interacts. Discrete relaxation is used to accelerate a backtrack search to find all vectors that satisfy all such constraints. Optimization is achieved by evaluating the objective function for all such vectors. This new method of optimization has been experimentally compared with a classical dynamic programming algorithm running with the same pseudorandomly generated objective functions. View full abstract»

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  • New Algorithm for the Slant Transform

    Page(s): 551 - 555
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    Two new algorithms that are more convenient for computation than existing ones for the slant transform are developed. These algorithms reveal the close relationship between the slant transform and the Walsh-Hadamard transform and demonstrate that the slant transform may be approached by a series of steps which gradually change the transform from a Hadamard or Walsh transform to a slant transform. View full abstract»

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  • Call for Papers

    Page(s): 556
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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