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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 6 • Date Jun 1989

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Displaying Results 1 - 13 of 13
  • Image enhancement for segmentation by self-induced autoregressive filtering

    Publication Year: 1989 , Page(s): 655 - 661
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (780 KB)  

    A filter induced by the autoregressive pattern from an image is used to enhance the image itself. The enhanced image usually reveals the wave pattern and nonstationary structures more clearly than the original. Unlike the Fourier analysis, local structures in the image are still retained. The merits have been demonstrated on simulated data and synthetic-aperture radar (SAR) ocean imagery View full abstract»

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  • Two-dimensional linear prediction model-based decorrelation method

    Publication Year: 1989 , Page(s): 661 - 665
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (508 KB)  

    A unified feature extraction scheme, the two-dimensional (2-D) linear prediction model-based decorrelation method, is presented. By applying 2-D causal linear prediction model to decorrelate a textured image, the very heavy computation load required when using a whitening operator to decorrelate the image, or the significant information loss when using the gradient operator to approximately whiten the image is avoided. The texture model-based decorrelation provides three sets of features to perform texture classification: the coefficients of the 2-D linear prediction, the moments of error residuals and the autocorrelation values. An optimum feature-selection scheme using modified branch-and-bound method was introduced to reduce information redundancy. After feature selection, 100% classification accuracy was achieved for a 20-class texture problem. Experiments show that this feature extraction scheme is truly information lossless, effective, and fast View full abstract»

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  • Recovering three-dimensional shape from a single image of curved objects

    Publication Year: 1989 , Page(s): 555 - 566
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1024 KB)  

    An algorithm to recover three-dimensional shape, i.e., surface orientation and relative depth from a single segmented image is presented. It is assumed that the scene is composed of opaque regular solid objects bounded by piecewise smooth surfaces with no markings or textures. It is also assumed that the reflectance map R(n ) is known. For the canonical case of Lambertian surfaces illuminated by a point light source, this implies knowing the light-source direction. A variational formulation of line drawing and shading constraints in a common framework is developed. The global constraints are partitioned into constraint sets corresponding to the faces, edges and vertices in the scene. For a face, the constraints are given by Horn's image irradiance equation. A variational formulation of the constraints at an edge both from the known direction of the image curve corresponding to the edge and shading is developed. At a vertex, the constraints are modeled by a system of nonlinear equations. An algorithm is presented to solve this system of constraints View full abstract»

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  • Principal warps: thin-plate splines and the decomposition of deformations

    Publication Year: 1989 , Page(s): 567 - 585
    Cited by:  Papers (550)  |  Patents (41)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1620 KB)  

    The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current in computer vision. How they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images in indicated View full abstract»

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  • On the recognition of curved objects

    Publication Year: 1989 , Page(s): 632 - 643
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1304 KB)  

    The problem of determining the identity and pose of occluded objects from noisy data is examined. Previous work has shown that local measurements of the position and surface orientation of small patches of an object's surface may be used in a constrained search process to solve this problem, for the case of rigid polygonal objects using 2-D sensory data, or rigid polyhedral objects using 3-D data. The recognition system is extended to recognize and locate curved objects. The extension is done in two dimensions, and applies to the recognition of 2-D objects from 2-D data, or to the recognition of the 3-D objects in stable positions from 2-D data View full abstract»

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  • Surface orientation from a projected grid

    Publication Year: 1989 , Page(s): 650 - 655
    Cited by:  Papers (15)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (556 KB)  

    Two simple methods are given for obtaining the surface shape using a projected grid. After the camera is calibrated to the 3-D workspace, the only input date needed for the computation of surface normals are grid intersect points in a single 2-D image. The first method performs nonlinear computations based on the distortion of the lengths of the grid edges and does not require a full calibration matrix. The second method requires that a full parallel projection model of the imaging is available, which enables it to compute 3-D normals using simple linear computations. The linear method performed better overall in the experiments, but both methods produced normals within 4-8° of known 3-D directions. These methods appear to be superior to methods based on shape-from-shading because the results are comparable, yet the equipment setup is simpler and the processing is not very sensitive to object reflectance View full abstract»

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  • A representation theory for morphological image and signal processing

    Publication Year: 1989 , Page(s): 586 - 599
    Cited by:  Papers (63)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1276 KB)  

    A unifying theory for many concepts and operations encountered in or related to morphological image and signal analysis is presented. The unification requires a set-theoretic methodology, where signals are modeled as sets, systems (signal transformations) are viewed as set mappings, and translational-invariant systems are uniquely characterized by special collections of input signals. This approach leads to a general representation theory, in which any translation-invariant, increasing, upper semicontinuous system can be presented exactly as a minimal nonlinear superposition of morphological erosions or dilations. The theory is used to analyze some special cases of image/signal analysis systems, such as morphological filters, median and order-statistic filters, linear filters, and shape recognition transforms. Although the developed theory is algebraic, its prototype operations are well suited for shape analysis; hence, the results also apply to systems that extract information about the geometrical structure of signals View full abstract»

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  • Range image segmentation based on differential geometry: a hybrid approach

    Publication Year: 1989 , Page(s): 643 - 649
    Cited by:  Papers (59)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (684 KB)  

    The authors describe a hybrid approach to the problem of image segmentation in range data analysis, where hybrid refers to a combination of both region- and edge-based considerations. The range image of 3-D objects is divided into surface primitives which are homogeneous in their intrinsic differential geometric properties and do not contain discontinuities in either depth of surface orientation. The method is based on the computation of partial derivatives, obtained by a selective local biquadratic surface fit. Then, by computing the Gaussian and mean curvatures, an initial region-gased segmentation is obtained in the form of a curvature sign map. Two additional initial edge-based segmentations are also computed from the partial derivatives and depth values, namely, jump and roof-edge maps. The three image maps are then combined to produce the final segmentation. Experimental results obtained for both synthetic and real range data of polyhedral and curved objects are given View full abstract»

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  • A shape from shading analysis for a single perspective image of a polyhedron

    Publication Year: 1989 , Page(s): 545 - 554
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB)  

    A shape-from-shading analysis for a single perspective image of a polyhedron is presented. Given a single perspective image of a polyhedron, the depth of any point of the polyhedron from the camera, the direction of the light source illuminating the polyhedron and the albedo of the polyhedron, a system of algebraic equations are derived, which, when combined with edge information, quantitatively describes the shape of the polyhedron. This analysis is best possible in the sense that if any component of what the author has assumed is omitted, no similar analysis can provide the same results View full abstract»

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  • A study of associative evidential reasoning

    Publication Year: 1989 , Page(s): 623 - 631
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (808 KB)  

    Associative evidential reasoning is the mechanism of combining evidence and evaluating hypotheses, which is the core of many computational systems. It is shown that under the generalized symmetry condition, that is, f(a,b)=neg (f(neg(a), neg(b))), where f is the combination operator satisfying common requirements like associativity and monotonicity, and neg maps positive elements to negative ones and vice versa, f is uniquely determined by a one-place mapping from the positive region to the set of positive reals. Furthermore, such combination formulas cannot be made robust, and quantizing the region will cause the loss of associativity or other inconsistencies. The implications on evidential reasoning system are: there exists often only one kind of formula for combining evidence; the quest for robust combination is often infeasible; and the attempt of converting numerical degrees of belief to linguistic quantifiers and vice versa is destined to be fruitless View full abstract»

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  • Efficient parallel algorithms for image template matching on hypercube SIMD machines

    Publication Year: 1989 , Page(s): 665 - 669
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB)  

    Efficient parallel algorithms developed on hypercube SIMD (single-instruction multiple data-stream) machines for image template matching are presented. Most of these parallel algorithms are asymptotically optimal in their time complexities. These results improve the known bounds in the literature View full abstract»

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  • Design of perimeter estimators for digitized planar shapes

    Publication Year: 1989 , Page(s): 611 - 622
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1000 KB)  

    Measurement of perimeters of planar shapes from their digitized images is an important task of computer vision systems. A general methodology for the design of simple and accurate parameter estimation algorithms is described. It is based on minimizing the maximum estimation error for digitized straight edges over all orientations. Two perimeter estimators are derived and their performance is tested and digitized circles using computer simulations. The experimental results may be used to predict the performance of the algorithm on shapes with arbitrary contours of continuous curvature. The simulations also show that fast and accurate perimeter estimation is possible, even for objects that are small relative to pixel size View full abstract»

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  • Optimal estimation of contour properties by cross-validated regularization

    Publication Year: 1989 , Page(s): 600 - 610
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (992 KB)  

    The problem of estimating the properties of smooth, continuous contours from discrete, noisy samples is used as vehicle to demonstrate the robustness of cross-validated regularization applied to a vision problem. A method for estimation of contour properties based on smoothing spline approximations is presented. Generalized cross-validation is to devise an automatic algorithm for finding the optimal value of the smoothing (regularization) parameter from the data. The cross-validated smoothing splines are then used to obtain optimal estimates of the derivatives of quantized contours. Experimental results are presented which demonstrate the robustness of the method applied to the estimation of curvature of quantized contours under variable scale, rotation, and partial occlusion. These results suggest the application of generalized cross-validation to other computer-vision algorithms involving regularization 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