A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions

By Calvin R. Maurer Jr., R. Qi, V. Raghavan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

968 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/712c3f11951b060dbf65db6101119fb7dfc28e21

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Citations in the corpus

Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks (2019, methodology): “In this work, we used the algorithm proposed in [37] for computing DT in 2D and the algorithm in [38] for experiments with 3D images.”
Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (2017, methodology): “eature function in conjunction with 99 other ones which include generalized means, lineal path function [42], 2-point correlation function, eective medium approximations [77] and distance transforms [65,49]. 3.1.2. The coarse-to-ne map p cf. For the coarse-to-ne map p cf(u fju c( c); cf), we employ ”
An Efficient Algorithm for Calculating the Exact Hausdorff Distance (2015, methodology): “There are various transforms depending on the distance metric used [18].”
Versatile and efficient pore network extraction method using marker-based watershed segmentation. (2017, methodology): “Many software packages offer this function, including MATLAB’s IMAGE PROCESSING TOOLBOX, SCIPY’s NDIMAGE module [42], and IMAGEJ [43].”
An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, neutral): “map, we first calculate the distance map [66], where each pixel”
3D Skeletons: A State‐of‐the‐Art Report (2016, background): “Such contact points are described by the so-called feature transform FT : O → P ( S ) [MQR03,HR08], deﬁned as which delivers the set of closest points y on a shape’s surface S to a given interior point x .”
Explicit Shape Encoding for Real-Time Instance Segmentation (2019, background): “Inner center The inner-center point is defined by the most far-way point from the contour, which can be obtained through distance transform [25].”
Distance Transforms of Sampled Functions (2012, neutral): “, [18, 7, 17]); however these methods are quite involved and are not widely used in practice.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “A simple approach to detect markers is DT, which assigns each pixel/voxel with the distance to the nearest feature point [49].”
2D Euclidean distance transform algorithms: A comparative survey (2008, methodology): “…DT in sequential computers have appeared since 1990, and new ones continue to be published [Lotufo and Zampirolli 2001; Saito and Toriwaki 1994; Maurer et al. 2003; Hirata 1996; Meijster et al. 2000; Boxer and Miller 2000; Eggers 1998; Ge and Fitzpatrick 1996; Gavrilova and Alsuwaiyel…”
The image foresting transform: theory, algorithms, and applications (2004, background): “Note, however, that propagation methods will hardly compete with other approaches based on the exact geometric Voronoi diagram, which can compute the exact EDT in linear time [48].”