Steerable filters for early vision, image analysis, and wavelet decomposition

An efficient architecture is presented to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively `steer´ a filter to any orientation, and to determine analytically the filter output as a function of orientation. The authors show how to design and steer filters, and present examples of their use in several tasks: the analysis of orientation and phase, angularly adaptive filtering, edge detection, and shape-from-shading. It is also possible to build a self-similar steerable pyramid representation which may be considered to be a steerable wavelet transform. The same concepts can be generalized to the design of 3-D steerable filters, which should be useful in the analysis of image sequences and volumetric data