“Q-MAT: Computing Medial Axis Transform By Quadratic Error Minimization” by Yan, Sykes, Chambers, Letscher and Ju

  • ©Yajie Yan, Kyle Sykes, Erin Chambers, David Letscher, and Tao Ju




    Q-MAT: Computing Medial Axis Transform By Quadratic Error Minimization

Session/Category Title:   GEOMETRY




    The medial axis transform (MAT) is an important shape representation for shape approximation, shape recognition, and shape retrieval. Despite years of research, there is still a lack of effective methods for efficient, robust and accurate computation of the MAT. We present an efficient method, called Q-MAT, that uses quadratic error minimization to compute a structurally simple, geometrically accurate, and compact representation of the MAT. We introduce a new error metric for approximation and a new quantitative characterization of unstable branches of the MAT, and integrate them in an extension of the well-known quadric error metric (QEM) framework for mesh decimation. Q-MAT is fast, removes insignificant unstable branches effectively, and produces a simple and accurate piecewise linear approximation of the MAT. The method is thoroughly validated and compared with existing methods for MAT computation.


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