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Optical Engineering

Three-dimensional object classification using shadow moire and neural network
Author(s): Mani Maran Ratnam; Chee Peng Lim; Ai Ni Khor
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Paper Abstract

A method of recognizing and classifying 3-D shapes with continuous surfaces by integrating shadow moire technique and neural network is presented. Unlike existing methods of 3-D shape recognition that use range images of polyhedral objects or objects of different geometries such as cones, rods, spheres, etc., the proposed method classifies continuous surfaces that are geometrically similar. The objects selected to test the classification method are eggs of four different grades. The shadow moire technique, which has greater sensitivity compared to structured lighting or laser scanning, is used to obtain moire patterns on the surface of the eggs. From the moire pattern images 14 parameters are extracted and used as input to a multilayer feedforward neural network. The results of the classification using the neural network show that the prediction accuracy attainable is 60% when classification is performed on all four grades. The accuracy increased to 95% when three of the grades are classified.

Paper Details

Date Published: 1 September 2001
PDF: 5 pages
Opt. Eng. 40(9) doi: 10.1117/1.1391260
Published in: Optical Engineering Volume 40, Issue 9
Show Author Affiliations
Mani Maran Ratnam, Univ. Sains Malaysia (Malaysia)
Chee Peng Lim, Univ. Sains Malaysia (Malaysia)
Ai Ni Khor, Univ. Sains Malaysia (Malaysia)

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