Transformation invariance is an important property in pattern recognition, where different observations of the same object typically receive the same label. This paper focuses on a transformation invariant distance measure that represents the minimum distance between the transformation manifolds spanned by patterns of interest. Since these manifolds are typically nonlinear, the computation of the manifold distance becomes a non-convex optimization problem. We propose to represent a pattern of interest as a linear combination of a few geometric functions extracted from a structured and redundant basis. Transforming the pattern results in the transformation of its constituent parts. We show that when the transformation is restricted to a synthesis of translations, rotations and isotropic scalings, such a pattern representation results in a closed- form expression of the manifold equation with respect to the transformation parameters. The manifold distance computation can be then formulated as a minimization problem, whose objective function is expressed as the difference of convex functions (DC). This interesting property permits to solve optimally the optimization problem with DC programming solvers that are globally convergent. We present experimental evidence which shows that our method is able to find the globally optimal solution, outperforming existing methods that yield suboptimal solutions.