View-based 3D object recognition using wavelet multiscale singular-value decomposition and support vector machine

In this paper, a novel view-based 3D object recognition method is proposed, which consists of three steps. First, employing wavelet transform to decompose view images of the object into different frequency sub-images. Second, for each sub-image, the features are extracted using singular-value decomposition (SVD) approach, and the features extracted from sub-images are combined to construct the feature vector of the original image. Third, the feature vector is fed into the support vector machine (SVM) to classify the objects. Experimental results show that the proposed method is effective.