Michael Kass, Justin Solomon
Abstract:
Local image histograms contain a great deal of information
useful for applications in computer graphics, computer vision
and computational photography. Making use of that information
has been challenging because of the expense of computing histogram
properties over large neighborhoods. Efficient algorithms
exist for some specific computations like the bilateral filter,
but not others. Here, we present an efficient
and practical method for computing accurate derivatives and
integrals of locally-weighted histograms over large neighborhoods. The method allows us to compute the location, height, width and integral
of all local histogram modes at interactive rates. Among other things,
it enables the first constant-time isotropic median filter, robust isotropic
image morphology operators, an efficient "dominant mode" filter and a non-iterative
alternative to the mean shift. In addition, we present a method to
combat the over-sharpening that is typical of histogram-based edge-preserving
smoothing. This post-processing step should make histogram-based filters not only fast
and efficient, but also suitable for a variety of new applications.
Paper (PDF)
To appear in the Proceedings of SIGGRAPH 2010
Available as Pixar Technical Memo #10-02