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Proceedings Paper

Landmark-based partial shape recognition by a BAM neural network
Author(s): Xiao-Jun Liu; Nirwan Ansari
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Paper Abstract

In this paper, we develop a bidirectional associative memory (BAM) based neural network to achieve high-speed partial shape recognition. To recognize objects which are partially occluded, we represent each object by a set of landmarks. The landmarks of an object are points of interest relative to the object that have important shape attributes. To achieve recognition, feature values (landmark values) of each model object are trained and stored in the network. Each memory cell is trained to store landmark values of a model object for all possible positions. Given a scene which may consist of several objects, landmarks in the scene are first extracted, and their corresponding landmark values are computed. Scene landmarks values are entered to each trained memory cell. The memory cell is shown to be able to recall the position of the model object in the scene. A heuristic measure is then computed to validate the recognition.

Paper Details

Date Published: 1 November 1991
PDF: 11 pages
Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50317
Show Author Affiliations
Xiao-Jun Liu, New Jersey Institute of Technology (United States)
Nirwan Ansari, New Jersey Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1606:
Visual Communications and Image Processing '91: Image Processing
Kou-Hu Tzou; Toshio Koga, Editor(s)

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