Method of normal estimation based on approximation for visualization

A normal estimation algorithm for visualization is pre- sented that approximates the density function in a local neighbor- hood with a second-degree polynomial function. The coefficients of the polynomial function can be solved by minimizing the error of the approximation. This method is tested in several volume data sets and comparisons with other methods are presented. It is demon- strated that this method is a fairly robust technique for noise- contained data and is preferable for most applications. © 2003 SPIE

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