NEW APPROACH FOR 3D OBJECT RECOGNITION USING NON UNIFORM FOURIER AND MOMENTS COEFFICIENTS

In this paper, new descriptors have been proposed which are invariant to rotation and affinity transformations (Non Uniform Fourier and moments coefficients). These descriptors are computed using Fourier or moments coefficients obtained from development of object coordinates. These coordinates are parameterized with a function also invariant to affinity. The normal Fourier transform depends on points index, the Non Uniform transform proposed here depend on a parameter. This parameter depends on shape not on order of points. The proposed descriptors are easy to extract and are extensible to higher dimension

Affine Shape Comparison using Different Distances

In this work, we propose to compare affine shape using Hausdorff distance (HD), Dynamic Time Warping (DTW), Frechet (DF), and Earth Mover distance (EMD). Where there is only a change in resolution shape distance are computed between shape coordinates because the distance is not invariant under rotation or affinity. In case of transformation, distances are calculated not between shape coordinates but between Arc length or Affine Arc length. Arc length is invariant under rotation while Affine Arc length is invariant under affinity. The main advantage is invariance under change of resolution, rotation, and affinity