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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 10 • Date Oct 1995

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Displaying Results 1 - 8 of 8
  • Logical/linear operators for image curves

    Publication Year: 1995 , Page(s): 982 - 996
    Cited by:  Papers (66)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1312 KB)  

    We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify aspects of linear operator theory and Boolean logic. A family of these operators appropriate for measuring the low-order differential structure of image curves is developed. These L/L operators are derived by decomposing a linear model into logical components to ensure that certain structural preconditions for the existence of an image curve are upheld. Tangential conditions guarantee continuity, while normal conditions select and categorize contrast profiles. The resulting operators allow for coarse measurement of curvilinear differential structure (orientation and curvature) while successfully segregating edge-and line-like features. By thus reducing the incidence of false-positive responses, these operators are a substantial improvement over (thresholded) linear operators which attempt to resolve the same class of features View full abstract»

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  • Person identification using multiple cues

    Publication Year: 1995 , Page(s): 955 - 966
    Cited by:  Papers (138)  |  Patents (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1176 KB)  

    This paper presents a person identification system based on acoustic and visual features. The system is organized as a set of non-homogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on visual ones provide data for an integration module whose performance is evaluated. A novel technique for the integration of multiple classifiers at an hybrid rank/measurement level is introduced using HyperBF networks. Two different methods for the rejection of an unknown person are introduced. The performance of the integrated system is shown to be superior to that of the acoustic and visual subsystems. The resulting identification system can be used to log personal access and, with minor modifications, as an identity verification system View full abstract»

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  • MINPRAN: a new robust estimator for computer vision

    Publication Year: 1995 , Page(s): 925 - 938
    Cited by:  Papers (80)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1260 KB)  

    MINPRAN is a new robust estimator capable of finding good fits in data sets containing more than 50% outliers. Unlike other techniques that handle large outlier percentages, MINPRAN does not rely on a known error bound for the good data. Instead, it assumes the bad data are randomly distributed within the dynamic range of the sensor. Based on this, MINPRAN uses random sampling to search for the fit and the inliers to the fit that are least likely to have occurred randomly. It runs in time O(N2+SN log N), where S is the number of random samples and N is the number of data points. We demonstrate analytically that MINPRAN distinguished good fits to random data and MINPRAN finds accurate fits and nearly the correct number of inliers, regardless of the percentage of true inliers. We confirm MINPRAN's properties experimentally on synthetic data and show it compares favorably to least median of squares. Finally, we apply MINPRAN to fitting planar surface patches and eliminating outliers in range data taken from complicated scenes View full abstract»

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  • Markov random field models for unsupervised segmentation of textured color images

    Publication Year: 1995 , Page(s): 939 - 954
    Cited by:  Papers (147)  |  Patents (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1136 KB)  

    We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation View full abstract»

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  • Composition of image analysis processes through object-centered hierarchical planning

    Publication Year: 1995 , Page(s): 997 - 1009
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1368 KB)  

    This paper presents a new approach to the knowledge-based composition of processes for image interpretation and analysis. Its computer implementation in the VISIPLAN (Vision Planner) system provides a way of modeling the composition of image analysis processes within a unified, object-centered hierarchical planning framework. The approach has been tested and shown to be flexible in handling a reasonably broad class of multi-modality biomedical image analysis and interpretation problems. It provides a relatively general design or planning framework, within which problem specific image analysis and recognition processes can be generated more efficiently and effectively. In this way, generality is gained at the design and planning stages, even though the final implementation stage of interpretation processes is almost invariably problem- and domain-specific View full abstract»

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  • The use of three- and four-dimensional surface harmonics for rigid and nonrigid shape recovery and representation

    Publication Year: 1995 , Page(s): 967 - 981
    Cited by:  Papers (26)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1396 KB)  

    The use of spherical harmonics for rigid and nonrigid shape representation is well known. This paper extends the method to surface harmonics defined on domains other than the sphere and to four-dimensional spherical harmonics. These harmonics enable us to represent shapes which cannot be represented as a global function in spherical coordinates, but can be in other coordinate systems. Prolate and oblate spheroidal harmonics and cylindrical harmonics are examples of surface harmonics which we find useful. Nonrigid shapes are represented as functions of space and time either by including the time-dependence as a separate factor or by using four-dimensional spherical harmonics. This paper compares the errors of fitting various surface harmonics to an assortment of synthetic and real data samples, both rigid and nonrigid. In all cases we use a linear least-squares approach to find the best fit to given range data. It is found that for some shapes there is a variation among geometries in the number of harmonics functions needed to achieve a desired accuracy. In particular, it was found that four-dimensional spherical harmonics provide an improved model of the motion of the left ventricle of the heart View full abstract»

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  • Optimal ℒ1 approximation of the Gaussian kernel with application to scale-space construction

    Publication Year: 1995 , Page(s): 1015 - 1019
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB)  

    Scale-space construction based on Gaussian filtering requires convolving signals with a large bank of Gaussian filters with different widths. We propose an efficient way for this purpose by L1 optimal approximation of the Gaussian kernel in terms of linear combinations of a small number of basis functions. Exploring total positivity of the Gaussian kernel, the method has the following properties: 1) the optimal basis functions are still Gaussian and can be obtained analytically; 2) scale-spaces for a continuum of scales can be computed easily; 3) a significant reduction in computation and storage costs is possible. Moreover, this work sheds light on some issues related to use of Gaussian models for multiscale image processing View full abstract»

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  • Texture classification using noncausal hidden Markov models

    Publication Year: 1995 , Page(s): 1010 - 1014
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB)  

    This paper addresses the problem of using noncausal hidden Markov models (HMMs) for texture classification. In noncausal models, the state of each pixel may be dependent on its neighbors in all directions. New algorithms are given to learn the parameters of a noncausal HMM of a texture and to classify it into one of several learned categories. Texture classification results using these algorithms are provided View full abstract»

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