2012Unpublished venueRequires access

Inserting control points for Bezier curve approximation

Jumei Zhang, Honglun Wang

Open publisher page 2 citations

Abstract

The master benefit of squared distance minimization is that it need not parameterize the data points of the target curve, so it converges faster than those method need points parameterization. Whereas, SDM can be very sensitive to the original curve owe to its local optimization. We propose a new approach to improve this. In this paper, we associate SDM with procedures for automatically inserting and redistributing of the control points of the active Bezier curve. The effectiveness and accuracy of our approach is illustrated in experimental example.

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What this paper is about

The master benefit of squared distance minimization is that it need not parameterize the data points of the target curve, so it converges faster than those method need points parameterization. Whereas, SDM can be very sensitive to the original curve owe to its local optimization. We propose a new approach to improve this. In this paper, we associate SDM with procedures for automatically inserting and redistributing of the control points of the active Bezier curve. The effectiveness and accuracy of our approach is illustrated in experimental example.

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Available abstract

The master benefit of squared distance minimization is that it need not parameterize the data points of the target curve, so it converges faster than those method need points parameterization. Whereas, SDM can be very sensitive to the original curve owe to its local optimization. We propose a new approach to improve this. In this paper, we associate SDM with procedures for automatically inserting and redistributing of the control points of the active Bezier curve. The effectiveness and accuracy of our approach is illustrated in experimental example.

Key concepts: Bézier curve, Curve fitting, Data point, Control point, Minification, Computer science, Mathematical optimization, Control (management)

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