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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 1 • Date Jan 1990

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Displaying Results 1 - 12 of 12
  • Determination of camera location from 2-D to 3-D line and point correspondences

    Page(s): 28 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (712 KB)  

    A method for the determination of camera location from two-dimensional (2-D) to three-dimensional (3-D) straight line or point correspondences is presented. With this method, the computations of the rotation matrix and the translation vector of the camera are separable. First, the rotation matrix is found by a linear algorithm using eight or more line correspondences, or by a nonlinear algorithm using three or more line correspondences, where the line correspondences are either given or derived from point correspondences. Then, the translation vector is obtained by solving a set of linear equations based on three or more line correspondences, or two or more point correspondences. Eight 2-D to 3-D line correspondences or six 2-D to 3-D point correspondences are needed for the linear approach; three 2-D to 3-D line or point correspondences for the nonlinear approach. Good results can be obtained in the presence of noise if more than the minimum required number of correspondences are used View full abstract»

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  • The quality of training sample estimates of the Bhattacharyya coefficient

    Page(s): 92 - 97
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (496 KB)  

    The quality, in terms of the bias and variance, of estimates of the Bhattacharyya coefficient based on n training samples from two classes described by multivariate Gaussian distributions is considered. The case where the classes are described by a common covariance matrix, as well as the case where each class is described by a different covariance matrix, is analyzed. Expressions for the bias and the variance of estimates of the Bhattacharyya coefficient are derived, and numerical examples are used to show the relationship between these parameters, the number of training samples, and the dimensionality of the observation space View full abstract»

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  • Morphological shape decomposition

    Page(s): 38 - 45
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    A technique for decomposing a binary shape into a union of simple binary shapes is presented. The decomposition is shown to be unique and invariant to translation, rotation, and scaling. The techniques used in the decomposition are based on mathematical morphology. The shape description produced can be used in object recognition and in binary image coding View full abstract»

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  • Fast algorithms for low-level vision

    Page(s): 78 - 87
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (912 KB)  

    A recursive filtering structure is proposed that drastically reduces the computational effort required for smoothing, performing the first and second directional derivatives, and carrying out the Laplacian of an image. These operations are done with a fixed number of multiplications and additions per output point independently of the size of the neighborhood considered. The key to the approach is, first, the use of an exponentially based filter family and, second, the use of the recursive filtering. Applications to edge detection problems and multiresolution techniques are considered, and an edge detector allowing the extraction of zero-crossings of an image with only 14 operations per output element at any resolution is proposed. Various experimental results are shown View full abstract»

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  • Feature point correspondence in the presence of occlusion

    Page(s): 87 - 91
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (572 KB)  

    Occlusion and poor feature point detection are two of the main difficulties in the use of multiple frames for establishing correspondence of feature points. A formulation of the correspondence problem as an optimization problem is used to handle these difficulties. Modifications to an existing iterative optimization procedure for solving the formulation of the correspondence problem are discussed. Experimental results are presented to show the merits of the formulation View full abstract»

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  • Segmentation of textured images and Gestalt organization using spatial/spatial-frequency representations

    Page(s): 1 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1236 KB)  

    The generic issue of clustering/grouping is addressed. Recent research, both in computer and human vision, suggests the use of joint spatial/spatial-frequency (s/sf) representations. The spectrogram, the difference of Gaussians representation, the Gabor representation, and the Wigner distribution are discussed and compared. It is noted that the Wigner distribution gives superior joint resolution. Experimental results in the area of texture segmentation and Gestalt grouping using the Wigner distribution are presented, proving the feasibility of using s/sf representations for low-level (early, preattentive) vision View full abstract»

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  • Application of the Karhunen-Loeve procedure for the characterization of human faces

    Page(s): 103 - 108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB)  

    The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion. This results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix, without increasing the complexity of the calculation. The resulting approximation of faces projected from outside of the data set onto this optimal basis is improved on average View full abstract»

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  • Scaling theorems for zero-crossings

    Page(s): 46 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    Two scaling theorems are given. Instead of delta functions, polynomial functions are used as input. The advantages are twofold: the smoothness conditions on kernels are not required, so that kernels of the form e-k|X| can be included; the proofs are based on calculus completely and so can be more easily understood View full abstract»

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  • A bivariate autoregressive technique for analysis and classification of planar shapes

    Page(s): 97 - 103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (608 KB)  

    A bivariate autoregressive model is introduced for the analysis and classification of closed planar shapes. The boundary coordinate sequence of a digitized binary image is sampled to produce a polygonal approximation to an object's shape. This circular sample sequence is then represented by a vector autoregressive difference equation which models the individual Cartesian coordinate sequences as well as coordinate interdependencies. Several classification features which are functions or transformations of the estimated coefficient matrices and the associated residual error covariance matrices are developed. These features are shown to be invariant to object transformations such as translation, rotation, and scaling. Laboratory experiments involving object sets representative of industrial shapes are presented. Superior classification results are demonstrated View full abstract»

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  • Boundary detection and skeletonization with a massively parallel architecture

    Page(s): 74 - 78
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    A massively parallel architecture called TOSCA (tokens sending cellular automation) is presented that performs edge pixel detection and skeletonization in the image processing area. Each cell of this cellular automaton has a very reduced set of instructions and a very small amount of memory. The computation is based on token propagation, counting devices, and local processing. The skeletonization method is based on the Chamfer distance View full abstract»

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  • Active tracking strategy for monocular depth inference over multiple frames

    Page(s): 13 - 27
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    The extraction of depth information from a sequence of images is investigated. An algorithm that exploits the constraint imposed by active motion of the camera is described. Within this framework, in order to facilitate measurement of the navigation parameters, a constrained egomotion strategy was adopted in which the position of the fixation point is stabilized during the navigation (in an anthropomorphic fashion). This constraint reduces the dimensionality of the parameter space without increasing the complexity of the equations. A further distinctive point is the use of two sampling rates: the faster (related to the computation of the instantaneous optical flow) is fast enough to allow the local operator to sense the passing edge (or, in other words, to allow the tracking of moving contour points), while the slower (used to perform the triangulation procedure necessary to derive depth) is slow enough to provide a sufficiently large baseline for triangulation. Experimental results on real image sequences are presented View full abstract»

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  • Multichannel texture analysis using localized spatial filters

    Page(s): 55 - 73
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    A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or carrier of region information, is emphasized. The channel filters used, known as the two-dimensional Gabor functions, are useful for these purposes in several senses: they have tunable orientation and radial frequency bandwidths and tunable center frequencies, and they optimally achieve joint resolution in space and in spatial frequency. By comparing the channel amplitude responses, one can detect boundaries between textures. Locating large variations in the channel phase responses allows discontinuities in the texture phase to be detected. Examples are given of both types of texture processing using a variety of real and synthetic textures 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