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Proceedings Paper

Moment invariants applied to the recognition of objects using neural networks
Author(s): Adilson Gonzaga; Jose Alfredo Ferreira Costa
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Paper Abstract

Visual pattern recognition and visual object recognition are central aspects of high level computer vision systems. This paper describes a method of recognizing patterns and objects in digital images with several types of objects in different positions. The moment invariants of such real work, noise containing images are processed by a neural network, which performs a pattern classification. Two learning methods are adopted for training the network: the conjugate gradient and the Levenber-Maquardt algorithms, both in conjunction with simulated annealing, for different sets of error conditions and features. Real images are used for testing the net's correct class assignments and rejections. We present results and comments focusing on the system's capacity to generalize, even in the presence of noise, geometrical transformations, object shadows and other types of image degradation. One advantage of the artificial neural network employed is its low execution time, allowing the system to be integrated to an assembly industry line for automated visual inspection.

Paper Details

Date Published: 14 November 1996
PDF: 11 pages
Proc. SPIE 2847, Applications of Digital Image Processing XIX, (14 November 1996); doi: 10.1117/12.258228
Show Author Affiliations
Adilson Gonzaga, Univ. of Sao Paulo (Brazil)
Jose Alfredo Ferreira Costa, Univ. of Sao Paulo (Brazil)

Published in SPIE Proceedings Vol. 2847:
Applications of Digital Image Processing XIX
Andrew G. Tescher, Editor(s)

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