Sharpening Orientation Selectivity for Efficient Image Filtering

The steering framework significantly reduces the computational cost of detecting and extracting oriented low-level spatial features in images by linearly combining the responses of a small set of basis filters at fixed orientations to yield filter responses at arbitrary orientations. However, the characteristics of the steered filter, such as orientation and scale selectivity, remain the same as those of the basis filters. We show that linear combination with the appropriate basis filters and weights can achieve more. We present a novel approach for sharpening the orientation selectivity of polar-separable filters by linear combination and well-known trigonometric identities which reduces the need for more basis filters with higher orientation selectivity. We describe the principles for and issues with designing quadrature sharpening filters. We also qualitatively and quantitatively compare 4-basis sharpened filter kernels with an equivalent steerable set of 6-basis filters