Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
The Shock Scaffold for Representing 3D Shape
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A skeletal measure of 2D shape similarity
Computer Vision and Image Understanding
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Strategies for shape matching using skeletons
Computer Vision and Image Understanding
Path Similarity Skeleton Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Bag of Paths for Kernel Based Shape Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
An online kernel change detection algorithm
IEEE Transactions on Signal Processing - Part II
Tree Covering within a Graph Kernel Framework for Shape Classification
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Shape similarity based on a treelet kernel with edition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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A large family of shape comparison methods is based on a medial axis transform combined with an encoding of the skeleton by a graph. Despite many qualities this encoding of shapes suffers from the non continuity of the medial axis transform. In this paper, we propose to integrate robustness against structural noise inside a graph kernel. This robustness is based on a selection of the paths according to their relevance and on path editions. This kernel is positive semi-definite and several experiments prove the efficiency of our approach compared to alternative kernels.