The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale estimation of vector field anisotropy application to texture characterization
Signal Processing - From signal processing theory to implementation
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating local multiple orientations
Signal Processing
A new adaptive framework for unbiased orientation estimation in textured images
Pattern Recognition
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
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This paper is concerned with the estimation of the dominant orientation of textured patches that appear in a number of images (remote sensing, biology or natural sciences for instance). It is based on the maximization of a criterion that deals with the coefficients enclosed in the different bands of a wavelet decomposition of the original image. More precisely, we search for the orientation that best concentrates the energy of the coefficients in a single direction. To compare the wavelet coefficients between the different bands, we use the Kullback-Leibler divergence on their distribution, this latter being assumed to behave like a Generalized Gaussian Density. The space-time localization of the wavelet transform allows to deal with any polygon that may be contained in a single image. This is of key importance when one works with (non-rectangular) segmented objects. We have applied the same methodology but using other criteria to compare the distributions, in order to highlight the benefit of the Kullback-Leibler divergence. In addition, the methodology is validated on synthetic and real situations and compared with a state-of-the-art approach devoted to orientation estimation.