The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of Multiparameter Steerable Functions Using Cascade Basis Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted median filters admitting complex-valued weights and their optimization
IEEE Transactions on Signal Processing - Part I
Optimal weighted median filtering under structural constraints
IEEE Transactions on Signal Processing
Recursive weighted median filters admitting negative weights andtheir optimization
IEEE Transactions on Signal Processing
Weighted Median Filters for Multichannel Signals
IEEE Transactions on Signal Processing
Tuning the smoothness of the recursive median filter
IEEE Transactions on Signal Processing
A general weighted median filter structure admitting negativeweights
IEEE Transactions on Signal Processing
Design of linear combination of weighted medians
IEEE Transactions on Signal Processing
Steerable wedge filters for local orientation analysis
IEEE Transactions on Image Processing
Generalized multichannel image-filtering structures
IEEE Transactions on Image Processing
Wavelet-based rotational invariant roughness features for texture classification and segmentation
IEEE Transactions on Image Processing
A steerable complex wavelet construction and its application to image denoising
IEEE Transactions on Image Processing
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Afilter is steerable if transformed (i.e., rotated, scaled, etc.) versions of its impulse response can be expressed as linear combinations of a fixed set of basis functions. Steerability is important for numerous image processing applications. However, it is a property presently shared only by a specific class of linear filters. On the other hand, several classes of nonlinear filters, such as weighted median filters (WMFs), may offer certain advantages over linear filters such as robustness and edge preserving capabilities. In this paper, the concept of steerability is extended to encompass WMFs. It will be shown that, in general, a steerable WMF design technique needs to be capable of handling negative weights. Although methods that allow the design of WMFs admitting negative weights have already been proposed, such methods do not necessarily produce filters that are steerable, as opposed to the approach presented in this work. Experimental results illustrate the applicability of steerable WMFs in two applications, namely edge detection and orientation analysis.