Three-dimensional object classification using shadow moire and neural network

A method of recognizing and classifying 3-D shapes with continuous surfaces by integrating shadow moire technique and neural network is presented. Unl ike existing methods of 3-D shape recognition that use range images of poly hedral objects or objects of different geometries such as cones, rods, sphe res, etc., the proposed method classifies continuous surfaces that are geom etrically similar. The objects selected to test the classification method a re eggs of four different grades. The shadow moire technique, which has gre ater sensitivity compared to structured lighting or laser scanning, is used to obtain moire patterns on the surface of the eggs. From the moire patter n images 14 parameters are extracted and used as input to a multilayer feed forward neural network. The results of the classification using the neural network show that the prediction accuracy attainable is 60% when classifica tion is performed on all four grades. The accuracy increased to 95% when th ree of the grades are classified. (C) 2001 Society of Photo-Optical Instrum entation Engineers.