Multiscale image understanding
Parallel computer vision
Evaluating digital angles by a parallel diffusion process
Pattern Recognition Letters
Constructing deterministic finite-state automata in recurrent neural networks
Journal of the ACM (JACM)
Learning Visual Models from Shape Contours Using Multiscale Convex/Concave Structure Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree Systems for Syntactic Pattern Recognition
IEEE Transactions on Computers
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Encoding nondeterministic fuzzy tree automata into recursive neural networks
IEEE Transactions on Neural Networks
Distributed recursive learning for shape recognition through multiscale trees
Image and Vision Computing
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In this paper 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, lenght, simmetry 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.