Affine Invariant Contour Descriptors Using Independent Component Analysis And Dyadic Wavelet Transform

摘要

The paper presents a novel technique for affine invariant feature extraction with the view of object recognition based on parameterized contour. The proposed technique first normalizes an input image by removing the affine deformations using independent component analysis which also reduces the noise introduced during contour parameterization. Then four invariant function-als are constructed using the restored object contour, dyadic wavelet transform and conies in the context of wavelets. Experimental results are conducted using three different standard datasets to confirm the validity of the proposed technique. Beside this, the error rates obtained in terms of invariant stability are significantly lower when compared to other wavelet-based invariants. Also the proposed invariants exhibit higher feature disparity than the method of Fourier descriptors.