  Shape- and scale-selective digital-filters, with steerable finite/infinite
impulse responses (FIR/IIRs) and non-recursive/recursive realizations, that are
separable in both spatial dimensions and adequately isotropic, are derived. The
filters are conveniently designed in the frequency domain via derivative
constraints at dc, which guarantees orthogonality and monomial selectivity in
the pixel domain (i.e. vanishing moments), unlike more commonly used FIR
filters derived from Gaussian functions. A two-stage low-pass/high-pass
architecture, for blur/derivative operations, is recommended. Expressions for
the coefficients of a low-order IIR blur filter with repeated poles are
provided, as a function of scale; discrete Butterworth (IIR), and colored
Savitzky-Golay (FIR), blurs are also examined. Parallel software
implementations on central processing units (CPUs) and graphics processing
units (GPUs), for scale-selective blob-detection in aerial surveillance
imagery, are analyzed. It is shown that recursive IIR filters are significantly
faster than non-recursive FIR filters when detecting large objects at coarse
scales, i.e. using filters with long impulse responses; however, the margin of
outperformance decreases as the degree of parallelization increases.
