Surface Thinning in 3D Cubical Complexes
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Parallel Banding Algorithm to compute exact distance transform with the GPU
Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
Robust skeletonization using the discrete λ-medial axis
Pattern Recognition Letters
A stable skeletonization for tabletop gesture recognition
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part I
Three-dimensional thinning algorithms on graphics processing units and multicore CPUs
Concurrency and Computation: Practice & Experience
Topological maps and robust hierarchical Euclidean skeletons in cubical complexes
Computer Vision and Image Understanding
A 3d curvilinear skeletonization algorithm with application to path tracing
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
Scale filtered euclidean medial axis
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
Empirical mode decomposition on skeletonization pruning
Image and Vision Computing
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A general algorithm for computing Euclidean skeletons of 2D and 3D data sets in linear time is presented. These skeletons are defined in terms of a new concept, called the integer medial axis (IMA) transform. We prove a number of fundamental properties of the IMA skeleton, and compare these with properties of the CMD (centers of maximal disks) skeleton. Several pruning methods for IMA skeletons are introduced (constant, linear and square-root pruning) and their properties studied. The algorithm for computing the IMA skeleton is based upon the feature transform, using a modification of a linear-time algorithm for Euclidean distance transforms. The skeletonization algorithm has a time complexity which is linear in the number of input points, and can be easily parallelized. We present experimental results for several data sets, looking at skeleton quality, memory usage and computation time, both for 2D images and 3D volumes.