Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
On embedding a graph in the grid with the minimum number of bends
SIAM Journal on Computing
Introduction to algorithms
The minimum broadcast time problem for several processor networks
Theoretical Computer Science
A unified linear-time algorithm for computing distance maps
Information Processing Letters
Finding the minimal set of maximum disks for binary objects
Graphical Models and Image Processing
Efficient shape representation by minimizing the set of centres of maximal discs/spheres
Pattern Recognition Letters
Handbook of discrete and computational geometry
Handbook of discrete and computational geometry
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Computer representation of planar regions by their skeletons
Communications of the ACM
Medial axis for chamfer distances: computing look-up tables and neighbuorhoods in 2D or 3D
Pattern Recognition Letters
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact medial axis with euclidean distance
Image and Vision Computing
Some theoretical challenges in digital geometry: A perspective
Discrete Applied Mathematics
Appearance radii in medial axis test mask for small planar chamfer norms
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
Fast distance transformation on irregular two-dimensional grids
Pattern Recognition
Sparse object representations by digital distance functions
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
| Hi-index | 5.23 |
The medial axis is a classical representation of digital objects widely used in many applications. However, such a set of balls may not be optimal: subsets of the medial axis may exist without changing the reversivility of the input shape representation. In this article, we first prove that finding a minimum medial axis is an NP-hard problem for the Euclidean distance. Then, we compare two algorithms which compute an approximation of the minimum medial axis, one of them providing bounded approximation results.