3-D Object Recognition using Multi-Wavelet and Neural Network

Abstract

This search has introduced the techniques of multi-wavelet transform and neural networkfor recognition 3-D object from 2-D image using patches. The proposed techniques were testedon database of different patches features and the high energy subband of discrete multi-wavelettransform DMWT (gp) of the patches. The test set has two groups, group (1) which containsimages, their (gp) patches and patches features of the same images as a part of that in the data setbeside other images, (gp) patches and features, and group (2) which contains the (gp) patches andpatches features the same as a part of that in the database but after modification such as rotation,scaling and translation. Recognition by back propagation (BP) neural network as compared withmatching by minimum distance, gave (94%) and (83%) score by using group (1), (gp) andfeatures respectively, which is much better than the minimum distance. Recognition using (gp)neural network (NN) gave a (94%) and (72%) score by using group (2), (gp) and featuresrespectively, while the minimum distance gave (11%) and (33%) scores. Time consumptionthrough the recognition process using (NN) with (gp) is less than that minimum distance