Nowadays, image analysis is becoming more important because of its ability to perform fast
and non-invasive low-cost analysis on products and processes. Image analysis is a wide …

In the framework of texture image retrieval, a new family of stochastic multivariate modeling
is proposed based on Gaussian Copula and wavelet decompositions. We take advantage of …

We present a functional framework for the design of tight steerable wavelet frames in any
number of dimensions. The 2-D version of the method can be viewed as a generalization of …

Our main goal in this paper is to set the foundations of a general continuous-domain
framework for designing steerable, reversible signal transformations (aka frames) in multiple …

We introduce a complete parameterization of the family of two-dimensional steerable
wavelets that are polar-separable in the Fourier domain under the constraint of self …

This paper contemplates the framework of probabilistic image retrieval in the wavelet
domain from a computational point of view. We not only focus on achieving high retrieval …

We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity
measure on the manifold of zero-mean multivariate generalized Gaussian distributions …

In this paper, we investigate a novel joint statistical model for subband coefficient
magnitudes of the dual-tree complex wavelet transform, which is then coupled to a Bayesian …

This dissertation investigates the system-centred evaluation of visual information retrieval
from generic photographic collections. The development of visual information retrieval …

We propose a texture learning approach that exploits local organizations of scales and
directions. First, linear combinations of Riesz wavelets are learned using kernel support …