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This paper presents a new affine-invariant matching algorithm based on B-spline modeling, which solves the problem of the nonuniqueness of B-spline in curve matching. This method first smoothes the B-spline curve by increasing the degree of the curve. It is followed by a reduction of the curve degree using the least square error (LSE) approach to construct the curvature scale space (CSS) image. CSS matching is then carried out. Our method combines the advantages of B-spline that are continuous curve representation and the robustness of CSS matching with respect to noise and affine transformation. It avoids the need for other matching algorithms that have to use the resampled points on the curve. Thus, the curve matching error is reduced. The proposed algorithm has been tested by matching similar shapes from a prototype database. The experimental results showed the robustness and accuracy of the proposed method in B-spline curve matching.
Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:29 , Issue: 10 )
Date of Publication: Oct. 2007