Dissimilarity between two skeletal trees in a context
Pattern Recognition
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Hierarchical Shape Decomposition via Level Sets
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Piecewise approximation of contours through scale-space selection of dominant points
IEEE Transactions on Image Processing
A similarity-based approach for shape classification using Aslan skeletons
Pattern Recognition Letters
Skeleton growing and pruning with bending potential ratio
Pattern Recognition
A game theoretic approach to learning shape categories and contextual similarities
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Co-transduction for shape retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Balancing deformability and discriminability for shape matching
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Skeleton simplification by key points identification
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
The Global-Local transformation for noise resistant shape representation
Computer Vision and Image Understanding
Visual pathways for shape abstraction
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
On Using Anisotropic Diffusion for Skeleton Extraction
International Journal of Computer Vision
Local phase quantization descriptor for improving shape retrieval/classification
Pattern Recognition Letters
Poisson Skeleton Revisited: a New Mathematical Perspective
Journal of Mathematical Imaging and Vision
From a Non-Local Ambrosio-Tortorelli Phase Field to a Randomized Part Hierarchy Tree
Journal of Mathematical Imaging and Vision
Hi-index | 0.14 |
We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on stable properties the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of the traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones.