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Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.


Papers
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Journal ArticleDOI
TL;DR: The decomposition scheme is illustrated using real data from different applications of which one, namely the identification of the three parts of the Immunoglobulin G antibody imaged using cryo electron tomography, is described more in detail.

25 citations

Journal ArticleDOI
TL;DR: This work uses a GPU to build an exact adaptive distance field, constructed from an octree by using the Morton code, and uses rectangle-swept spheres to construct a bounding volume hierarchy (BVH) around a triangulated model.
Abstract: Most techniques for real-time construction of a signed distance field, whether on a CPU or GPU, involve approximate distances. We use a GPU to build an exact adaptive distance field, constructed from an octree by using the Morton code. We use rectangle-swept spheres to construct a bounding volume hierarchy (BVH) around a triangulated model. To speed up BVH construction, we can use a multi-BVH structure to improve the workload balance between GPU processors. An upper bound on distance to the model provided by the octree itself allows us to reduce the number of BVHs involved in determining the distances from the centers of octree nodes at successively lower levels, prior to an exact distance query involving the remaining BVHs. Distance fields can be constructed 35-64 times as fast as a serial CPU implementation of a similar algorithm, allowing us to simulate a piece of fabric interacting with the Stanford Bunny at 20 frames per second.

25 citations

Journal ArticleDOI
01 Nov 2007
TL;DR: The spatially-adaptive method is highly resilient to shot noise since global, generalized Coulomb potentials can be used to disregard the presence of outliers due to noise, and thus they convey global information which is crucial in the fitting process.
Abstract: We propose a novel, geometrically adaptive method for surface reconstruction from noisy and sparse point clouds, without orientation information. The method employs a fast convection algorithm to attract the evolving surface towards the data points. The force field in which the surface is convected is based on generalized Coulomb potentials evaluated on an adaptive grid (i.e., an octree) using a fast, hierarchical algorithm. Formulating reconstruction as a convection problem in a velocity field generated by Coulomb potentials offers a number of advantages. Unlike methods which compute the distance from the data set to the implicit surface, which are sensitive to noise due to the very reliance on the distance transform, our method is highly resilient to shot noise since global, generalized Coulomb potentials can be used to disregard the presence of outliers due to noise. Coulomb potentials represent long-range interactions that consider all data points at once, and thus they convey global information which is crucial in the fitting process. Both the spatial and temporal complexities of our spatially-adaptive method are proportional to the size of the reconstructed object, which makes our method compare favorably with respect to previous approaches in terms of speed and flexibility. Experiments with sparse as well as noisy data sets show that the method is capable of delivering crisp and detailed yet smooth surfaces.

25 citations

Journal ArticleDOI
TL;DR: This work develops a parallel algorithm for the three-dimensional Euclidean distance transform (3D-EDT) on the EREW PRAM computation model and implements the proposed algorithms sequentially, the performance of which exceeds the existing algorithms.
Abstract: In a two- or three-dimensional image array, the computation of Euclidean distance transform (EDT) is an important task. With the increasing application of 3D voxel images, it is useful to consider the distance transform of a 3D digital image array. Because the EDT computation is a global operation, it is prohibitively time consuming when performing the EDT for image processing. In order to provide the efficient transform computations, parallelism is employed. We first derive several important geometry relations and properties among parallel planes. We then, develop a parallel algorithm for the three-dimensional Euclidean distance transform (3D-EDT) on the EREW PRAM computation model. The time complexity of our parallel algorithm is O(log/sup 2/ N) for an N/spl times/N/spl times/N image array and this is currently the best known result. A generalized parallel algorithm for the 3D-EDT is also proposed. We implement the proposed algorithms sequentially, the performance of which exceeds the existing algorithms (proposed by Yamada, 1984). Finally, we develop the corresponding parallel programs on both the emulated EREW PRAM model computer and the IBM SP2 to verify the speed-up properties of the proposed algorithms.

25 citations

Patent
15 Feb 1999
TL;DR: In this paper, a method of searching a database of 3D protein structures was proposed, which comprises the steps of setting a three-dimensional protein structure, forming a two-dimensional binary distance map based on the 3D structure, and comparing the one-dimensional peripheral distribution of a protein structure with that of another protein structure a dynamic programming algorithm.
Abstract: A method of searching a database of three-dimensional protein structures. The method comprises the steps of setting a three-dimensional protein structure; forming a two-dimensional binary distance map based on the three-dimensional protein structure; forming a one-dimensional peripheral distribution based on the distance map; and comparing the one-dimensional peripheral distribution of a protein structure with that of another protein structure a dynamic programming algorithm. The method increases detection sensitivity and search speed.

25 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20235
202217
202161
202099
2019112
201881