Determination of the number of texture segments using wavelets

This paper presents a robust method of determining the number of texture segments in an image. We take an $N imes N$ image and decompose it into $n imes n$ blocks. A three-scale two-dimensional discrete wavelet transform is performed on each $n imes n$ block. For each $n imes n$ block, this transformation produces coefficients for 25 wavelet channels. The energy of each channel is used as a tuple for a vector in the feature space. Nearest neighbor clustering is used to segment the feature space. A measure is defined to determine the ``goodness" of the clustering. The optimal number of segments is taken to be the clustering which maximizes our measure

Phase Algorithm for Blocking Artifact Reduction in Reconstructions from Analysis-Only AM-FM Models

Virtually all techniques for computing AM-FM models involve jointly localized filterbanks and nonlinear approximations that preclude the possibility of perfect reconstruction. Although perfect reconstruction models have begun to emerge, they are currently of limited practical interest because of their large complexity. Thus, it is of interest to obtain approximate reconstructions from the analysis-only models because these provide insight into the accuracy of the AM and FM functions. However, the only existing 2-D reconstruction algorithm suffers from severe blocking artifacts that limit its usefulness in this regard. In this paper we present a new reconstruction algorithm that makes full use of all available phase reconstruction boundary conditions in a multipath interpolative scheme to eliminate the blocking artifacts, thereby dramatically improving the utility of the approximate reconstructions. 1