Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Power diagrams: properties, algorithms and applications
SIAM Journal on Computing
A new characterization of three-dimensional simple points
Pattern Recognition Letters
Detection of 3-D Simple Points for Topology Preserving Transformations with Application to Thinning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified linear-time algorithm for computing distance maps
Information Processing Letters
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy distance transform: theory, algorithms, and applications
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact medial axis with euclidean distance
Image and Vision Computing
Fast and Accurate Approximation of the Euclidean Opening Function in Arbitrary Dimension
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
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We present a fast and accurate approximation of the Euclidean thickness distribution computation of a binary shape in arbitrary dimension. Thickness functions associate a value representing the local thickness for each point of a binary shape. When considering with the Euclidean metric, a simple definition is to associate with each point x, the radius of the largest ball inscribed in the shape containing x. Such thickness distributions are widely used in many applications such as medical imaging or material sciences and direct implementations could be time consuming. In this paper, we focus on fast algorithms to extract such distribution on shapes in arbitrary dimension.