Computing 3D Medial Axis for Chamfer Distances
نویسندگان
چکیده
منابع مشابه
Medial axis lookup table and test neighborhood computation for 3D chamfer norms
Chamfer distances are discrete distances based on the propagation of local distances, or weights, defined in a mask. The medial axis, i.e. the centers of maximal balls (balls which are not contained in any other ball), is a powerful tool for shape representation and analysis. The extraction of maximal disks is performed in the general case by testing the inclusion of a ball in a local neighborh...
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Chamfer distances are discrete distances based on the propagation of local distances, or weights defined in a mask. The medial axis, i.e. the centers of the maximal disks (disks which are not contained in any other disk), is a powerful tool for shape representation and analysis. The extraction of maximal disks is performed in the general case with comparison tests involving look-up tables repre...
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The test mask TM is the minimum neighbourhood sufficient to extract the medial axis of any discrete shape, for a given chamfer distance mask M. We propose an arithmetical framework to study TM in the case of chamfer norms. We characterize TM for 3×3 and 5×5 chamfer norm masks, and we give an algorithm to compute the appearance radius of the vector (2, 1) in TM.
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I In this paper, a new filter is designed to enhance medial-axis of tubular structures. Based on a multi-scale method and using eigenvectors of Hessian matrix, the distance of a point to the edges of the tube is found. To do this, a hypothetical line with a deliberate direction is passed through the point which cuts the tube at its edges. For points which are located on the medial-axis, this di...
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Medial Axis (MA), also known as Centres of Maximal Disks, is a useful representation of a shape for image description and analysis. MA can be computed on a distance transform, where each point is labelled to its distance to the background. Recent algorithms allow one to compute Squared Euclidean Distance Transform (SEDT) in linear time in any dimension. While these algorithms provide exact meas...
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تاریخ انتشار 2000