TY - GEN
T1 - Design of accurate and smooth filters for function and derivative reconstruction
AU - Möller, Torsten
AU - Mueller, Klaus
AU - Kurzion, Yair
AU - Machiraju, Raghu
AU - Yagel, Roni
N1 - Publisher Copyright:
Copyright 1998 IEEE.
PY - 1998/10/1
Y1 - 1998/10/1
N2 - The correct choice of function and derivative reconstruction filters is paramount to obtaining highly accurate renderings. Most filter choices are limited to a set of commonly used functions, and the visualization practitioner has so far no way to state his preferences in a convenient fashion. Much work has been done towards the design and specification of filters using frequency based methods. However, for visualization algorithms it is more natural to specify a filter in terms of the smoothness of the resulting reconstructed function and the spatial reconstruction error. Hence, in this paper, we present a methodology for designing filters based on spatial smoothness and accuracy criteria. We first state our design criteria and then provide an example of a filter design exercise. We also use the filters so designed for volume rendering of sampled data sets and a synthetic lest function. We demonstrate that our results compare favorably with existing methods.
AB - The correct choice of function and derivative reconstruction filters is paramount to obtaining highly accurate renderings. Most filter choices are limited to a set of commonly used functions, and the visualization practitioner has so far no way to state his preferences in a convenient fashion. Much work has been done towards the design and specification of filters using frequency based methods. However, for visualization algorithms it is more natural to specify a filter in terms of the smoothness of the resulting reconstructed function and the spatial reconstruction error. Hence, in this paper, we present a methodology for designing filters based on spatial smoothness and accuracy criteria. We first state our design criteria and then provide an example of a filter design exercise. We also use the filters so designed for volume rendering of sampled data sets and a synthetic lest function. We demonstrate that our results compare favorably with existing methods.
KW - Approximation (G.1.2)
KW - Interpolation (G.1.1)
KW - Picture/image generation (1.3.3)
KW - Quadrature and numerical differentiation (G.1.4)
KW - Reconstruction (1.4.5)
UR - https://www.scopus.com/pages/publications/85022118384
U2 - 10.1145/288126.288189
DO - 10.1145/288126.288189
M3 - Conference contribution
AN - SCOPUS:85022118384
T3 - Proceedings of the 1998 IEEE Symposium on Volume Visualization, VVS 1998
SP - 143
EP - 151
BT - Proceedings of the 1998 IEEE Symposium on Volume Visualization, VVS 1998
PB - Association for Computing Machinery, Inc
T2 - 1998 IEEE Symposium on Volume Visualization, VVS 1998
Y2 - 19 October 1998 through 20 October 1998
ER -