| Abstract: |
| Images are commonly represented as bitmaps, making it essential to identify the intrinsic geometric features of objects from such unstructured data. Vectorization is a widely used technique that converts raster images into collections of parametric curves and surfaces, capturing the input`s prominent features while yielding resolution-independent representations. In this talk, we propose variational principles for image vectorization, together with efficient algorithms based on the affine shortening flow and region merging, generalizing steepest gradient descent for the reduced Mumford-Shah functional. We also present recent applications in shape classification and historical glyph preservation. |
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