A new technique for real-time distortion-invariant multiobject recognition and classification

A real-time hybrid distortion-invariant OPR system was established to make 3-D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network (ANN) was used in correlation signal post-processing to perform multiobject distort ion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library (RFL) was constructed for the distortion version of 3-D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post-processing, the nonlinear algorithm of optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate were obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.