Distance transformations in arbitrary dimensions

@article{Borgefors1984DistanceTI,
  title={Distance transformations in arbitrary dimensions},
  author={Gunilla Borgefors},
  journal={Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing},
  year={1984},
  volume={27},
  pages={321-345}
}
  • G. Borgefors
  • Published 1 September 1984
  • Computer Science
  • Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
Some Weighted Distance Transforms in Four Dimensions
TLDR
In this paper, optimal real and integer weights are computed for one type of 4D weighted distance transforms.
Chamfer Distances with Integer Neighborhoods
TLDR
Results of a systematic search for approximations of the Euclidean distance in the two-dimensional case for neighborhoods of sizes up to 21 × 21 and scaling factors up to 1000 are presented.
Distance transformations in digital images
  • G. Borgefors
  • Computer Science
    Comput. Vis. Graph. Image Process.
  • 1986
Weighted digital distance transforms in four dimensions
On Digital Distance Transforms in Three Dimensions
TLDR
A new type of valid distance transforms (DTs) have been discovered, where optimality is defined as minimizing the maximum difference from the true Euclidean distance, thus making the DTs as direction independent as possible.
Some Sequential Algorithms for a Generalized Distance Transformation Based on Minkowski Operations
TLDR
A generalized distance transformation (GDT) of binary images and the related medial axis transformation (MAT) are discussed and different sequential algorithms are proposed for computing such GDTs.
Optimum design of chamfer distance transforms
TLDR
A new concept of critical local distances is presented which reduces the computational complexity of the chamfer distance transform without increasing the maximum approximation error.
The Distance Transform and its Computation
TLDR
How the distance transform has to be used in achieving the goals of these applications is discussed; instead, it concentrates on the basics of distances and on algorithms for computing a distance transformation in an appropriate manner.
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The matching of image and map features is performed rapidly by a new technique, called "chamfer matching", that compares the shapes of two collections of shape fragments, at a cost proportional to linear dimension, rather than area.
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