By Topic

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 4 • Date Oct. 1979

Filter Results

Displaying Results 1 - 22 of 22
  • [Front cover]

    Page(s): c1
    Save to Project icon | Request Permissions | PDF file iconPDF (731 KB)  
    Freely Available from IEEE
  • List of Contributors

    Page(s): nil1
    Save to Project icon | Request Permissions | PDF file iconPDF (250 KB)  
    Freely Available from IEEE
  • [Breaker page]

    Page(s): nil1
    Save to Project icon | Request Permissions | PDF file iconPDF (250 KB)  
    Freely Available from IEEE
  • A Coding Method of Chinese Characters

    Page(s): 333 - 341
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1293 KB)  

    In this paper we propose a coding method of handprinted Chinese characters in which, defining a grammar, the process of block categorization is made to correspond to a process of sentential generation. We obtain two strings of production rules and block codes being equivalent to the descriptions of the structure and internal components of a character pattern, and a Chinese character is encoded effectively by the use of these two strings. It is shown that this coding method is available for the classification and discrimination of handprinted Chinese characters. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • DECA: A Discrete-Valued Data Clustering Algorithm

    Page(s): 342 - 349
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1884 KB)  

    This paper presents a new clustering algorithm for analyzing unordered discrete-valued data. This algorithm consists of a cluster initiation phase and a sample regrouping phase. The first phase is based on a data-directed valley detection process utilizing the optimal second-order product approximation of high-order discrete probability distribution, together with a distance measure for discrete-valued data. As for the second phase, it involves the iterative application of the Bayes' decision rule based on subgroup discrete distributions. Since probability is used as its major decision criterion, the proposed method minimizes the disadvantages of yielding solutions sensitive to the arbitrary distance measure adopted. The performance of the proposed algorithm is evaluated by applying it to four different sets of simulated data and a set of clinical data. For performance comparison, the decision-directed algorithm [11] is also applied to the same set of data. These evaluation experiments fully demonstrate the validity and the operational feasibility of the proposed algorithm and its superiority as compared to the decision-directed algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Capacity and Error Estimates for Boolean Classifiers with Limited Complexity

    Page(s): 350 - 356
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1969 KB)  

    This paper extends the notions of capacity and distribution-free error estimation to nonlinear Boolean classifiers on patterns with binary-valued features. We establish quantitative relationships between the dimensionality of the feature vectors (d), the combinational complexity of the decision rule (c), the number of samples in the training set (n), and the classification performance of the resulting classifier. Our results state that the discriminating capacity of Boolean classifiers is given by the product dc, and the probability of ambiguous generalization is asymptotically given by (n/dc-1)-1 0(log d)/d) for large d, and n=0(dc). In addition we show that if a fraction ¿ of the training samples is misclassified then the probability of error (¿) in subsequent samples satisfies P(|¿-¿| ¿) m=<2.773 exp (dc-e2n/8) for all distributions, regardless of how the classifier was discovered. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Sinusoidal Family of Unitary Transforms

    Page(s): 356 - 365
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2377 KB)  

    A new family of unitary transforms is introduced. It is shown that the well-known discrete Fourier, cosine, sine, and the Karhunen-Loeve (KL) (for first-order stationary Markov processes) transforms are members of this family. All the member transforms of this family are sinusoidal sequences that are asymptotically equivalent. For finite-length data, these transforms provide different approximations to the KL transform of the said data. From the theory of these transforms some well-known facts about orthogonal transforms are easily explained and some widely misunderstood concepts are brought to light. For example, the near-optimal behavior of the even discrete cosine transform to the KL transform of first-order Markov processes is explained and, at the same time, it is shown that this transform is not always such a good (or near-optimal) approximation to the above-mentioned KL transform. It is also shown that each member of the sinusoidal family is the KL transform of a unique, first-order, non-stationary (in general), Markov process. Asymptotic equivalence and other interesting properties of these transforms can be studied by analyzing the underlying Markov processes. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Asymptotic Properties of Discrete Unitary Transforms

    Page(s): 366 - 371
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1668 KB)  

    A method for studying the asymptotic behavior of discrete transformations is developed using numerical quadrature theory. This method allows a more convenient examination of the correlation properties of common unitary transforms for large block sizes. As a practical result of this method it is shown that the discrete cosine transform is asymptotically optimal for all finite-order Markov signals. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images

    Page(s): 372 - 384
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2928 KB)  

    Effective and elegant procedures have recently appeared in the published literature for determining by computer a highly variable blob boundary in a noisy image [1]-[3]. In this paper we point out that if the blob boundary is modeled as a Markov process and the additive noise is modeled as a white Gaussian noise field, then maximization of the joint likelihood of the hypothesized blob boundary and all of the image data results in roughly the same blob boundary determination as does one of the aforementioned deterministic formulations [2]. However, the formulation in this paper provides insights into and optimal parameter values for the functions involved and reveals suboptimalities in some of the formulations appearing in the literature. More generally, we agree that maximization of the joint likelihood of the hypothesized blob boundary and of the entire picture function is a fundamental approach to boundary estimation or the estimation of linear features (roads, rivers, etc.) in images, and provides a powerful mechanism for designing sequential, parallel, or other boundary estimation algorithms. The ripple filter, an advanced form of region growing, is briefly introduced and illustrates one of a number of alternative algorithms for maximizing the likelihood function. Hence, this likelihood maximization approach provides a unified view for seemingly different approaches, such as sequential boundary finding and region growing. Bounds on the accuracy of boundary estimation are readily derived with this formulation and are presented. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Digital Image and Spectrum Restoration by Quadratic Programming and by Modified Fourier Transformation

    Page(s): 385 - 399
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4224 KB)  

    We consider the convolution equation f * h + e = d, where f is sought, h is a known ``point spread function,'' e represents random errors, and d is the measured data. All these functions are defined on the integers mod(N). A mathematical-statistical fonnulation of the problem leads to minff * hdA, where the A-norm is derived from the statistical distribution of e. If f is known to be nonnegative, this is a quadratic progamming problem. Using the discrete Fourier transforms (DFT's) F, H, and D of f, h, and d, we arrive at a minimization in another norm: minF F · H-D ¿. A solution would be F = D/H, but H has zeros. We consider the theoretical and practical difficulties that arise from these zeros and describe two methods for calculating F numerically also when H has zeros. Numerical tests of the methods are presented, in particular tests with one of the methods, called ``the derivative method,'' where d is a blurred image. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Variation on a Nonparametric Clustering Method

    Page(s): 400 - 408
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1092 KB)  

    A single modification to a mode-seeking clustering algorithm proposed by Koontz, Narendra, and Fukunaga is shown to generate a novel clustering and to provide an indication of cluster stability. The modified method should provide better clusterings for ``uniform, touching'' clusters than the original, although the original should work better than the modified method for ``touching Gaussian'' clusters. Suitable ranges for the clustering parameters of both methods are investigated. Since the modification requires changing only one line of the original algorithm, two clusterings can be obtained for the price of one coding. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Full text access may be available. Click article title to sign in or learn about subscription options.
  • On the Nonseparability of Image Models

    Page(s): 409 - 411
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (458 KB)  

    Jain and Angel's proof of nonseparability of the nearest neighbor model is discussed. A simple and helpful method is given to investigate the (non)separability of the autocorrelation of arbitrary two-dimensional image models. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Dual Method for Maximum Entropy Restoration

    Page(s): 411 - 414
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (700 KB)  

    A simple iterative dual algorithm for maximum entropy image restoration is presented. The dual algorithm involves fewer parameters than conventional minimization in the image space. Mini-computer test results for Fourier synthesis with inadequate phantom data are given. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Simplified Derivation of Frei's Histogram Hyperbolization for Image Enhancement

    Page(s): 414 - 415
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (438 KB)  

    Frei has recently introduced a new technique (histogram hyperbolization) for image enhancement by the manipulation of the picture brightness levels. An alternative derivation of Frei's result, both simpler and more general, is presented. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Some Decision Problems for Bottom-Up Triangle Acceptors

    Page(s): 415 - 417
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (506 KB)  

    It is proved that the emptiness problem for deterministic bottom-up triangle acceptors (BTA's) over a single input symbol is recursively unsolvable. From this result, it is also shown that certain decision problems involving BTA's and BPA's (deterministic bottom-up pyramid acceptors) are unsolvable. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Full text access may be available. Click article title to sign in or learn about subscription options.
  • Full text access may be available. Click article title to sign in or learn about subscription options.
  • 1979 Index - IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-1

    Page(s): 418 - 422
    Save to Project icon | Request Permissions | PDF file iconPDF (1078 KB)  
    Freely Available from IEEE
  • [Advertisement]

    Page(s): 423 - 424
    Save to Project icon | Request Permissions | PDF file iconPDF (227 KB)  
    Freely Available from IEEE
  • List of Contributors

    Page(s): nil2
    Save to Project icon | Request Permissions | PDF file iconPDF (148 KB)  
    Freely Available from IEEE
  • [Front cover]

    Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (1655 KB)  
    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.

Full Aims & Scope

Meet Our Editors

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