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Journal ArticleDOI

Shape Matching of Two-Dimensional Objects

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TLDR
The technique is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels, and has been applied to two-dimensional simple closed curves represented by polygons.
Abstract
In this paper we present results in the areas of shape matching of nonoccluded and occluded two-dimensional objects. Shape matching is viewed as a ``segment matching'' problem. Unlike the previous work, the technique is based on a stochastic labeling procedure which explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. To reduce the computation time, the technique is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. This basic technique has been extended to the situation where various objects partially occlude each other to form an apparent object and our interest is to find all the objects participating in the occlusion. In such a case several hierarchical processes are executed in parallel for every object participating in the occlusion and are coordinated in such a way that the same segment of the apparent object is not matched to the segments of different actual objects. These techniques have been applied to two-dimensional simple closed curves represented by polygons and the power of the techniques is demonstrated by the examples taken from synthetic, aerial, industrial and biological images where the matching is done after using the actual segmentation methods.

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Citations
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References
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Journal ArticleDOI

Generalizing the hough transform to detect arbitrary shapes

TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
Journal ArticleDOI

On the Encoding of Arbitrary Geometric Configurations

TL;DR: It is shown that one can determine through the use of relatively simple numerical techniques whether a given arbitrary plane curve is open or closed, whether it is singly or multiply connected, and what area it encloses.
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The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints

TL;DR: The gradient projection method was originally presented to the American Mathematical Society for solving linear programming problems by Dantzig et al. as discussed by the authors, and has been applied to nonlinear programming problems as well.
Journal ArticleDOI

On the Foundations of Relaxation Labeling Processes

TL;DR: It is shown that the problem of finding consistent labelings is equivalent to solving a variational inequality, and a procedure nearly identical to the relaxation operator derived under restricted circum-stances serves in the more general setting.