Steerable wedge filters for local orientation analysis

Steerable Wedge Filters

Steerable lters, as developed by Freeman and Adelson, are a class of rotation-invariant linear operators that may be used to analyze local orientation patterns in imagery. The most common examples of such operators are directional derivatives of Gaussians and their 2-D Hilbert transforms. The inherent symmetry of these lters produces an orientation response that is periodic with period , even w...

Local orientation analysis in images and image sequences using steerable filters

In this thesis, we address the issue of local orientation analysis using steerable filters. From the standpoint of the sampling theory, current orientation steerable filters sample the spectrum of the orientation space with Dirac functions. According to the well known uncertainty principle, we cannot simultaneously localize a signal both in the spatial domain and in the spectral domain exactly....

Errata and Comments on Image Analysis using Orientation Space based on Steerable Filters

Image Analysis using Orientation Space based on Steerable Filters

Oblique Random Forests for 3-D Vessel Detection Using Steerable Filters and Orthogonal Subspace Filtering

We propose a machine learning-based framework using oblique random forests for 3-D vessel segmentation. Two different kinds of features are compared. One is based on orthogonal subspace filtering where we learn 3-D eigenspace filters from local image patches that return task optimal feature responses. The other uses a specific set of steerable filters that show, qualitatively, similarities to t...