CSDL Home C CVPRW 2008 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
Mark Foskey , Medical Image Display&Analysis Group (MIDAG) University of North Carolina at Chapel Hill, USA
Joshua H. Levy , Medical Image Display&Analysis Group (MIDAG) University of North Carolina at Chapel Hill, USA
We introduce a locally defined shape-maintaining method for interpolating between corresponding oriented samples (vertices) from a pair of surfaces. We have applied this method to interpolate synthetic data sets in two and three dimensions and to interpolate medially represented shape models of anatomical objects in three dimensions. In the plane, each oriented vertex follows a circular arc as if it was rotating to its destination. In three dimensions, each oriented vertex moves along a helical path that combines in-plane rotation with translation along the axis of rotation. We show that our planar method provides shape-maintaining interpolations when the reference and target objects are similar. Moreover, the interpolations are size maintaining when the reference and target objects are congruent. In three dimensions, similar objects are interpolated by an affine transformation. We use measurements of the fractional anisotropy of such global affine transformations to demonstrate that our method is generally more-shape preserving than the alternative of interpolating vertices along linear paths irrespective of changes in orientation. In both two and three dimensions we have experimental evidence that when non-shape-preserving deformations are applied to template shapes, the interpolation tends to be visually satisfying with each intermediate object appearing to belong to the same class of objects as the end points.
Mark Foskey, Joshua H. Levy, "Rotational flows for interpolation between sampled surfaces", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563017