3-D Object Recognition System using Ultrasound

Abstract

The patterns of ultrasonic reflected echoes from objects contain information about the geometric shape, size, orientation and the surface material properties of the reflector. Accurate estimation of the ultrasonic echo signal pattern is essential for recognition of the target object. We propose a method to classify different objects having specific geometric shape such as cylindrical, rectangular, sphere and conical of different size and material. Here continuous wavelet transform (CWT) has been used for feature extraction. In the present work an attempt has been made to classify the pattern inherent in the features extracted through CWT of different echo signals with the help of two different machine learning algorithms like self organizing feature map (SOFM) and support vector machine (SVM). CWT allows a time domain signal to be transformed into time frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of features from the non-stationary signals like ultrasonic echo envelop. SOFM transforms the input of arbitrary dimension into a one or two dimensional discrete map subject to a topological (neighbourhood preserving) constraint. In the present work the SOFM algorithm with Kohonen's learning and SVM in regression mode has been used to classify the patterns inherent in the features extracted through CWT of different echo envelop