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

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 functionals are constructed using the restored object contour, dyadic wavelet transform and conics 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

An application of wavelet based affine invariant representation

Recently, a novel technique used to construct an affine-invariant representation based on the dyadic wavelet transform was proposed and tested on synthesised 2D contours (Tieng and Boles, 1994). In this paper, the performance of this representation with real objects under perspective transformation is considered. In comparison with a similarity invariant (curvature) representation, experimental results show that the proposed affine-invariant representation is the most appropriate one to represent and recognise objects under perspective transform