The aim of this paper is mainly on using the potential strength of the skeleton of discrete objects in computer vision and pattern recognition. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of different objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and effective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and occlusions.
Attributed skeletal graphs for shape modelling and matching
2003-01-01
297461 byte
Conference paper
Electronic Resource
English
Attributed Skeletal Graphs for Shape Modelling and Matching
British Library Conference Proceedings | 2003
|Extended Attributed String Matching for Shape Recognition
British Library Online Contents | 1998
|Shock Graphs and Shape Matching
British Library Conference Proceedings | 1998
|Attributed hypergraph matching on a Riemannian manifold
British Library Online Contents | 2014
|Shock Graphs and Shape Matching
British Library Online Contents | 1999
|