We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown, as is the shape of the surface. Given several pairs of point sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman's chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.

We present an automatization of Barnsley's manual algorithm for the solution of the inverse problem of iterated function systems (IFSs). The problem is to retrieve the number of mappings and the parameters of an IFS from a digital binary image approximating the attractor induced by the IFS. M.F. Barnsley et al. described a way to solve manually the inverse problem by identifying the fragments of which the collage is composed, and then computing the parameters of the mappings (Barnsley et al., Proc. Nat. Acad. Sci. USA, vol.83, p.1975-7, 1986; Barnsley, "Fractals Everywhere", Academic, 1988; Barnsley and Hurd, L., "Fractal Image Compression", A.K. Peters, 1992). The automatic algorithm searches through a finite set of points in the parameter space determining a set of affine mappings. The algorithm uses the collage theorem and the Hausdorff metric. The inverse problem of IFSs is related to the image coding of binary images. If the number of mappings and the parameters of an IFS, with not too many mappings, could be obtained from a binary image, then this would give an efficient representation of the image. It is shown that the inverse problem solved by the automatic algorithm has a solution and some experiments show that the automatic algorithm is able to retrieve an IFS, including the number of mappings, from a digital binary image approximating the attractor induced by the IFS.

Recursive image subsampling which yields support areas approaching fractals is described and analyzed using iterated function systems. The subsampling scheme is suitable in, e.g., hierarchical image processing and image coding schemes. For hexagonally sampled images a hierarchical subsampling structure is given which yields hexagon-like regions with fractal borders.