LINEAR FEATURE EXTRACTION TECHNIQUES FOR OBJECT RECOGNITION: STUDY OF PCA AND ICA
C. L. Chowdhary UDC: (004.932.72'1)
In this paper, we have compared linear techniques for object recognition. 3D object recognition is the process of matching an object to a scene description to determine the objects identity and / or its pose in space. Several face recognition techniques uses unsupervised statistical methods. The basic idea is to compute the principal components as sequence of image vectors incrementally, without estimating the covariance matrix and at the same time transforming these principal components to the independent directions that maximize the non–Gaussianity of the source. We illustrate the potential of PCA and ICA on a database of 1440 images of 20 different objects captured by CCD camera. The excellent recognition rates achieved in all the performed experiments indicate that the present method is well suited for appearance-based 3D object recognition and pose estimation.