  A method for quantitative analysis of local pattern strength and defects in
surface self-assembly imaging is presented and applied to images of stripe and
hexagonal ordered domains. The presented method uses "shapelet" functions which
were originally developed for quantitative analysis of images of galaxies
($\propto 10^{20}\mathrm{m}$). In this work, they are used instead to quantify
the presence of translational order in surface self-assembled films ($\propto
10^{-9}\mathrm{m}$) through reformulation into "steerable" filters. The
resulting method is both computationally efficient (with respect to the number
of filter evaluations), robust to variation in pattern feature shape, and,
unlike previous approaches, is applicable to a wide variety of pattern types.
An application of the method is presented which uses a nearest-neighbour
analysis to distinguish between uniform (defect-free) and non-uniform
(strained, defect-containing) regions within imaged self-assembled domains,
both with striped and hexagonal patterns.
