Hierarchical Shape Description Via the Multiresolution Symmetric Axis Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
IAPR Proceedings of the international workshop on Visual form: analysis and recognition
Continuous skeleton computation by Voronoi diagram
CVGIP: Image Understanding
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Pruning Discrete and Semiocontinuous Skeletons
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
Automatic Animation Skeleton Construction Using Repulsive Force Field
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Shape Simplification Based on the Medial Axis Transform
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Proceedings of the twenty-fifth annual symposium on Computational geometry
Players and ball detection in soccer videos based on color segmentation and shape analysis
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Voronoi-Based curve reconstruction: issues and solutions
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
Robust reconstruction of 2D curves from scattered noisy point data
Computer-Aided Design
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This paper presents a novel technique for medial axis noise removal. The method introduced removes the branches generated by noise on an object's boundary without losing the fine features that are often altered or destroyed by current pruning methods. The algorithm consists of an intuitive threshold-based pruning process, followed by an automatic feature reconstruction phase that effectively recovers lost details without reintroducing noise. The result is a technique that is robus and easy to use. Tests show that the method works well on a variety of objects with significant difference in shape complexity, topology and noise characteristics.