Hancock et al. [ 3 ] presented a probabilistic relaxation method for more consistent edge labelling but using a multi-resolution, hierarchical representation of the edge map. In this scheme, the probabilistic relaxation approach is applied to each level of resolution as if it were the single level approach, with the only difference that the ``prior probabilities'' of the labels at each level are adjusted to be consistent with the priors of the previous level as extracted from the dictionaries. Our scheme differs in the sense that it applies across different levels and it uses the same dictionary of allowable label configurations at all levels. Furthermore, we have a tower of images smoothed at different scales rather than a pyramid where the physical dimensions of the image are reduced.
Such multiscale approaches for texture analysis are few and far between. Unser and Eden [ 12 ] extracted texture energy measures form the image and smoothed the output of the extraction filter bank using Gaussian smoothing at different scales. The features in these multiscale planes are reduced, by diagonalising scatter matrices evaluated at two different spatial resolutions, and thresholded to yield texture segmentation. Matalas et al. [ 5 ], used a B-spline transform in order to obtain images at several smoothing levels to calculate vector dispersion and gradient orientation at different scales. A small disparity function was then applied to segment textures.
Dr. Majid Mirmehdi