Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
A thinning algorithm based on contours
Computer Vision, Graphics, and Image Processing
Fundamentals of digital image processing
Fundamentals of digital image processing
Simulating the Grassfire Transform Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous skeleton computation by Voronoi diagram
CVGIP: Image Understanding
On the Generation of Skeletons from Discrete Euclidean Distance Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
FORMS: a flexible object recognition and modelling system
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Representation and Self-Similarity of Shapes
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A skeletal measure of 2D shape similarity
Computer Vision and Image Understanding
An Axis-Based Representation for Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A skeleton family generator via physics-based deformable models
IEEE Transactions on Image Processing
The Groupwise Medial Axis Transform for Fuzzy Skeletonization and Pruning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skeleton growing and pruning with bending potential ratio
Pattern Recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
This paper presents a novel skeleton pruning approach based on a 2D empirical mode like decomposition (EMD-like). The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the object contour is decomposed by empirical mode like decomposition (EMD-like), the IMFs of the object provide a workspace with very good properties for obtaining the object's skeleton. The theoretical properties and the performed experiments demonstrate that the obtained skeletons match to hand-labeled skeletons provided by human subjects. Even in the presence of significant noise and shape variations, cuts and tears, the resulted skeletons have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches as many existing skeleton pruning methods and moreover, does not displace the skeleton points, which are all centers of maximal disks.