This work presents a new multiscale, curvaturebased shape representation technique for planar curves. One limitation of the well-known Curvature Scale Space (CSS) method is that it uses only curvature zero-crossings to characterize shapes and thus there is no CSS descriptor for convex shapes. The proposed method, on the other hand, uses bidimentional - unidimentional - bidimentional transformations together with resampling techniques to retain the full curvature information for shape characterization. It also employs the correlation coefficient as a measure of similarity. In the evaluation tests, the proposed method achieved a high correct classification rate (CCR), even when the shapes were severely corrupted by noise. Results clearly showed that the proposed method is more robust to noise than CSS.