The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting nod...
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The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting node, transitions are made to the goal nodes by a maximum likelihood metric. This metric requires only local calculations on the search space and its use in edge linking provides more accurate results than other linking techniques.< >
We address the problem of reconstructing a smooth curve from sparse and noisy information that is invariant to the choice of the coordinate system. Tikhonov regularization is used to form a well-posed mathematical pro...
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We address the problem of reconstructing a smooth curve from sparse and noisy information that is invariant to the choice of the coordinate system. Tikhonov regularization is used to form a well-posed mathematical problem statement, and conditions for an invariant reconstruction are given. The resulting functional minimization problem is shown to be nonconvex. Approximations to the invariant functional are often used to form a convex problem that can be solved efficiently. Two common approximations, those of cubic and weighted cubic splines, are detailed, and examples are given to show that the approximations are often invalid. To form a valid approximation to the invariant functional we propose a two-step algorithm. The first step forms a piecewise-linear curve, which is invariant to the coordinate system. This piecewise-linear curve is then used to construct a parameterization of the curve for which we can make a valid approximation to the invariant functional. Examples are given to demonstrate the effectiveness of the algorithm, and two example applications for which the invariant property is important are given.
A technique is presented that can be used in the visualization and analysis of cardiac wall motion abnormalities by digital two-dimensional echocardiography. This technique is based on the use of a curvature function ...
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A technique is presented that can be used in the visualization and analysis of cardiac wall motion abnormalities by digital two-dimensional echocardiography. This technique is based on the use of a curvature function extracted from the endocardial boundary locations and can be used for shape or shape change analysis of the heart. The authors call the locations of high absolute curvature landmarks. Identification of landmarks on the endocardial boundary provides a simplified but powerful description of the boundary that allows visualization and analysis of wall motion. The simplification of the heart beating process is an excellent tool for the identification of infarcted areas of the heart.
Many image compression techniques involve segmentation of a gray level image. With such techniques, information is extracted that describes the regions in the segmented image, and this information is then used to form...
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A new class of morphological filters is proposed for smoothing an image contaminated with noise. A multiple model that includes the combination of linear and nonlinear operations is used in the design of the new filte...
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A new class of morphological filters is proposed for smoothing an image contaminated with noise. A multiple model that includes the combination of linear and nonlinear operations is used in the design of the new filter. The performance of the averaging version of this new filter is similar to that of the alpha-trimmed mean filter. The structure-preserving properties of this new filter depend on the values assigned to the coefficients in the filter. The idempotent property is obtained when a closing-min and opening-max version of the filter is used. The root structure of the output signal is also investigated.< >
This study addresses a problem that arises in the early stages of visual processing. Low-level visionprocessing tasks generate information describing distances and orientations at sparse locations of visible surfaces...
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This study addresses a problem that arises in the early stages of visual processing. Low-level visionprocessing tasks generate information describing distances and orientations at sparse locations of visible surfaces in a scene. The information from these various low-level tasks need to be combined to form an estimate of the visual surface for all points in the scene. This step is commonly referred to as the surface reconstruction problem. The problem is cast as an ill-posed inverse problem, which must be stabilized using a priori information relative to image and constraint formation. A desirable property of any reconstruction algorithm is invariance with respect to rigid transformations of the surface in three-dimensional space. Two surface reconstruction algorithms that are based on invariant surface characteristics are proposed. These algorithms are studied in detail, and an example is presented to demonstrate their effectiveness. The discrete realization of the algorithms is also briefly discussed.< >
Edge detection is analyzed as a problem in cost minimization. A cost function is formulated that evaluates the quality of edge configurations. A mathematical description of edges is given, and the cost function is ana...
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Edge detection is analyzed as a problem in cost minimization. A cost function is formulated that evaluates the quality of edge configurations. A mathematical description of edges is given, and the cost function is analyzed in terms of the characteristics of the edges in minimum-cost configurations. The cost function is minimized by the simulated annealing method. A novel set of strategies for generating candidate states and a suitable temperature schedule are presented. Sequential and parallel versions of the annealing algorithm are implemented and compared. Experimental results are presented.< >
An efficient iterative method is proposed to grow and prune classification trees. This method divides the data sample into two subsets and iteratively grows a tree with one subset and prunes it with the other subset, ...
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An efficient iterative method is proposed to grow and prune classification trees. This method divides the data sample into two subsets and iteratively grows a tree with one subset and prunes it with the other subset, successively interchanging the roles of the two subsets. The convergence and other properties of the algorithm are established. Theoretical and practical considerations suggest that the iterative tree growing and pruning algorithm should perform better and require less computation than other widely used tree growing and pruning algorithms. Numerical results on a waveform recognition problem are presented to support this view.< >
A technique to guide landmark matching known as hopping dynamic programming is described. The location of the model in the scene is estimated with a least-squares fit. A heuristic measure is then computed to decide if...
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A technique to guide landmark matching known as hopping dynamic programming is described. The location of the model in the scene is estimated with a least-squares fit. A heuristic measure is then computed to decide if the model is in the scene. The shape features of an object are the landmarks associated with the object. The landmarks of an object are defined as the points of interest of the object that have important shape attributes. Examples of landmarks are corners, holes, protrusions, and high-curvature points.< >
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