Reflection coefficient (Schur parameter) representation for convex compact sets in the plane

We obtain a one-to-one relation between the shape and orientation of a convex compact planar set and a complex-valued reflection coefficient (Schur (1917) parameter) sequence, such that (1) the reflection coefficient magnitudes are less than or equal to one, (2) if any reflection coefficient has a magnitude equal to one, then all subsequent reflection coefficients are equal to zero, and (3) the first reflection coefficient is equal to zero. Three additional independent parameters specify the position of the set in the plane, and the size of the set (specifically its circumference). For a finite duration reflection coefficient sequence, if the last nonzero reflection coefficient has a magnitude that is less than one, then the boundary of the set is an infinitely differentiable convex curve. The boundary is a convex polygon if and only if the magnitude of the last reflection coefficient is equal to one; the number of sides of the polygon is equal to the index of the last reflection coefficient. Almost all planar convex compact sets have reflection coefficient sequences of infinite duration. Such sets can be accurately approximated with convex compact sets that are generated from relatively small numbers of reflection coefficients.

[1]  Ja-Chen Lin,et al.  A 1 log N parallel algorithm for detecting convex hulls on image boards , 1998, IEEE Trans. Image Process..

[2]  P. Delsarte,et al.  Half-plane Toeplitz systems , 1980, IEEE Trans. Inf. Theory.

[3]  W. Moore,et al.  Foundations of mechanical accuracy , 1970 .

[4]  Rolland Theodore Hinkle Kinematics of machines , 1953 .

[5]  F. B. Hildebrand Advanced Calculus for Applications , 1962 .

[6]  Patrick L. Combettes,et al.  Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections , 1997, IEEE Trans. Image Process..

[7]  Thomas L. Marzetta Reflection coefficient representation for convex planar sets , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[8]  Thomas L. Marzetta,et al.  A surprising Radon transform result and its application to motion detection , 1999, IEEE Trans. Image Process..

[9]  Charles M. Rader,et al.  Digital processing of signals , 1983 .

[10]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[11]  John E. Markel,et al.  Linear Prediction of Speech , 1976, Communication and Cybernetics.

[12]  J. P. Burg,et al.  Maximum entropy spectral analysis. , 1967 .

[13]  H. Landau Maximum entropy and the moment problem , 1987 .

[14]  T. Marzetta Two-dimensional linear prediction: Autocorrelation arrays, minimum-phase prediction error filters, and reflection coefficient arrays , 1980 .

[15]  W. M. Carey,et al.  Digital spectral analysis: with applications , 1986 .

[16]  C. K. Yuen,et al.  Digital spectral analysis , 1979 .

[17]  Jerry L. Prince,et al.  Reconstructing Convex Sets from Support Line Measurements , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[19]  T. Marzetta Additive and multiplicative minimum-phase decompositions of 2-D rational power density spectra , 1982 .

[20]  Nikolas P. Galatsanos,et al.  Removal of compression artifacts using projections onto convex sets and line process modeling , 1997, IEEE Trans. Image Process..

[21]  T. Kailath,et al.  On a generalized Szegö- Levinson realization algorithm for optimal linear predictors based on a network synthesis approach , 1978 .

[22]  P. Schultz,et al.  Fundamentals of geophysical data processing , 1979 .

[23]  Thomas L. Marzetta A linear prediction approach to two-dimensional spectral factorization and spectral estimation , 1978 .

[24]  J. Schur,et al.  Über Potenzreihen, die im Innern des Einheitskreises beschränkt sind. , 1917 .