Improved neural classifier for microscrew shape recognition

Authors:
A. Martin-Gonzalez;T. Baidyk;E. Kussul;O. Makeyev
Affiliations:
Universidad Nacional Autónoma de México, IIMAS, México, México;Universidad Nacional Autónoma de México, CCADET, México, México;Universidad Nacional Autónoma de México, CCADET, México, México;Clarkson University, New York, USA
Venue:
Optical Memory and Neural Networks
Year:
2010

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Abstract

We propose a neural network based vision system for attending micropieces manufacturing process in micromechanics. The system permits us to recognize the shape of micropieces (screws of 3 mm diameter) in order to get information for controlling and improving the manufacturing process. The neural classifier used for the shape recognition task is termed Limited Receptive Area Grayscale (LIRA Grayscale). The developed vision system has a recognition rate of 98.90%. This work is motivated by the idea of obtaining an automated control system for micromachines. This paper contains a detailed description of the model and learning rules, and discusses future perspectives.