Landmark-based shape recognition by a modified Hopfield neural network

Document Type

Article

Publication Date

1-1-1993

Abstract

A new method to recognize partially visible two-dimensional objects by means of a modified Hopfield neural network is introduced. Each object is represented by a set of "landmarks", and thus such an approach is referred to as landmark-based shape recognition. The landmarks of an object are points of interest relative to the object that have important shape attributes. Given a scene consisting of partially visible objects, a model object in the scene is determined by how well the model landmarks are matched to those in the scene. A local shape measure, known as the sphericity, is used to measure the similarity between two landmarks. The hypothesis of a model object in a scene is made by matching the model landmarks with the scene landmarks. The landmark matching task is performed by a modified Hopfield neural network. The location of the model in the scene is estimated by a least squares fit among the matched landmarks. The hypothesis is finally verified by a heuristic measure. The convergence of the modified Hopfield neural network is proven, and the robustness of the approach has been experimentally demonstrated. © 1993.

Identifier

0027579373 (Scopus)

Publication Title

Pattern Recognition

External Full Text Location

https://doi.org/10.1016/0031-3203(93)90108-9

ISSN

00313203

First Page

531

Last Page

542

Issue

4

Volume

26

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