We present an efficient and fully parallel 2D object recognition method based on the use of a multiscale tree representation of the object boundary and recursive learning of trees. Specifically, the object is represented by means of a tree where each node, corresponding to a boundary segment at some level of resolution, is characterized by a real vector containing curvature, length, and symmetry of the boundary segment, while the nodes are connected by arcs when segments at successive levels are spatially related. The recognition procedure is formulated as a training procedure made by recursive neural networks followed by a testing procedure over unknown tree structured patterns.
Shape recognition by distributed recursive learning of multiscale trees
01.01.2003
409008 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Shape Recognition by Distributed Recursive Learning of Multiscale Trees
| British Library Conference Proceedings | 2003
Distributed recursive learning for shape recognition through multiscale trees
| British Library Online Contents | 2007
Shape Recognition Using Recursive Mathematical Morphology
| British Library Conference Proceedings | 1997
Shape Representation and Recognition from Multiscale Curvature
| British Library Online Contents | 1997