Boundary Estimation from Intensity/Color Images with Algebraic Curve Models

A new concept and algorithm are presented for noniterative robust estimation of piecewise smooth curves of maximal edge strength in small image windows – typically to . This boundary-estimation algorithm has the nice properties that it uses all the data in the window and thus can find locally weak boundaries embedded in noise or texture and boundaries when there are more than two regions to be segmented in a window; it does not require step edges – but handles ramp edges well. The curve-estimates found are among the level sets of a d’th degree polynomial fit to ”suitable” weightings of the image gradient vector at each pixel in the image window. Since the polynomial fitting is linear least squares, the computation to this point is very fast. Level sets then chosen to be appropriate boundary curves are those having the highest differences in average gray level in regions to either side. This computation is also fast. The boundary curves and segmented regions found are suitable for all purposes but especially for indexing using algebraic curve invariants in this form. Acknowledgment This work was partially supported by NSF Grants #IIS-9802392 and #BCS-9980091.