Eulidean Skeletons

We present a new method for the skeletonization of 2-dimensional or 3-dimensional objects. First, we introduce two local measures, and d, to characterize skeleton points, whose good localizaa tion is ensured by Euclidean distance mapping techniques. These measures allow us to control the detail of the resulting skeleton. Thresholding these measures generally does not yield well-deened skeleton: a low threshold preserves the original object's topology but produces a noise-sensitive skeleton, while a higher threshold produces a more robust skeleton but it is generally not homotopic with the original object it comes from. To overcome these drawbacks, more complex measures can be introduced. Although they generally yield good experimental results, they are still sensitive to noise. Instead, we introduce a global step, called topological reconstruction, which will provide the skeleton with robustness with respect to noise and ensure homotopy with the original object. Moreover, this method is not iterative (like thinning approaches) and hence has reasonable compuu tational times for 3D objects; and it yields a representation of the original object which allows it to be reconstructed.