Automatic classification of products in the industry via invariant boundary moments
DOI:
https://doi.org/10.15381/idata.v10i2.6256Keywords:
Artificial intelligence, invariant pattern recognition, cybernetic vision, boundary moments, automatic classification, industrial applications.Abstract
The technique of the Invariant Boundary Moments (IBM) is applied to the automatic classification of two different randomly selected objects, independently of their size, position and orientation. It is shown that if the objects differ only in position and orientation (size is maintained), the power of the IBM is optimum; however when variations in size are included, overlapping results in the IBM show up, placing strong limitations to their use as a classifier tool, in cases like these a predefined margin of tolerance must be introduced.
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Copyright (c) 2007 Javier Montenegro Joo
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