Finding local maxima in a pseudo-Euclidean distance transform
Computer Vision, Graphics, and Image Processing
The Euclidean distance transform in arbitrary dimensions
Pattern Recognition Letters
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
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete bisector function and Euclidean skeleton in 2D and 3D
Image and Vision Computing
Finding a minimum medial axis of a discrete shape is NP-hard
Theoretical Computer Science
Discrete 2D and 3D euclidean medial axis in higher resolution
Image and Vision Computing
Farey Sequences and the Planar Euclidean Medial Axis Test Mask
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Surface Thinning in 3D Cubical Complexes
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
An implicit active contour model for feature regions and lines
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
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
Hole filling in 3D volumetric objects
Pattern Recognition
New reduced discrete Euclidean nD medial axis with optimal algorithm
Pattern Recognition Letters
Robust skeletonization using the discrete λ-medial axis
Pattern Recognition Letters
Chordal axis on weighted distance transforms
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
Exact euclidean medial axis in higher resolution
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
Fast and accurate approximation of digital shape thickness distribution in arbitrary dimension
Computer Vision and Image Understanding
Topological maps and robust hierarchical Euclidean skeletons in cubical complexes
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Medial Axis (MA), also known as Centres of Maximal Disks, is a useful representation of a shape for image description and analysis. MA can be computed on a distance transform, where each point is labelled to its distance to the background. Recent algorithms allow one to compute Squared Euclidean Distance Transform (SEDT) in linear time in any dimension. While these algorithms provide exact measures, the only known method to characterise MA on SEDT, using local tests and Look-Up Tables (LUT), is limited to 2D and small distance values [Borgefors, et al., Seventh Scandinavian Conference on Image Analysis, 1991]. We have proposed [Remy, et al., Pat. Rec. Lett. 23 (2002) 649] an algorithm which computes the LUT and the neighbourhood to be tested in the case of chamfer distances. In this article, we adapt our algorithm for SEDT in arbitrary dimension and show that results have completely different properties.