Affine Normalized Invariant functionals using Independent Component Analysis

The paper presents a hybrid technique for affine invariant feature extraction with the view of object recognition based on parameterized contour. The presented technique first normalizes an input image by removing affine distortions using independent component analysis which also reduces the effect of noise introduced during contour parameterization. Then two invariant functionals are constructed, one using the normalized object contour and the other using the dyadic wavelet transform. Experimental results conducted using three different standard datasets confirm the validity of the proposed approach. Beside this the error rates obtained in terms of invariant stability are significantly lower when compared to other wavelet based techniques and the proposed invariants exhibit higher feature disparity than the method of Fourier descriptors.