The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Y. Rao - One of the best experts on this subject based on the ideXlab platform.
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Double-step incremental Linear Interpolation
ACM Transactions on Graphics (TOG), 1992Co-Authors: Jon G. Rokne, Y. RaoAbstract:A two-step incremental Linear Interpolation algorithm is derived and analyzed. It is shown that the algorithm is correct, that it is reversible, and that it is faster than previous single-step algorithms. An example is given of the execution of the algorithm.
Alberto Leon-garcia - One of the best experts on this subject based on the ideXlab platform.
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Linear Interpolation lattice for nonstationary signals
IEEE Transactions on Signal Processing, 1993Co-Authors: M.r.k. Khansari, Alberto Leon-garciaAbstract:A ladder algorithm for Linear Interpolation of nonstationary signals is developed. The algorithm is based on the sliding-window least-squares method and can be implemented using a lattice structure. Furthermore, by assuming that the input signal is stationary, the number of parameters required to be calculated is reduced. The lattice structure in the case of stationary input is also presented. >
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A fast algorithm for optimal Linear Interpolation
IEEE Transactions on Signal Processing, 1993Co-Authors: M.r.k. Khansari, Alberto Leon-garciaAbstract:A fast algorithm for computing the optimal Linear Interpolation filter is developed. The algorithm is based on the Sherman-Morrison inversion formula for symmetric matrices. The relationship between the derived algorithm and the Levinson algorithm is illustrated. It is shown that the new algorithm, in comparison with the well-known algorithms, requires fewer multiplications and hence is of lower complexity. >
Jon G. Rokne - One of the best experts on this subject based on the ideXlab platform.
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Applying Rounding‐Up Integral Linear Interpolation to the Scan‐Conversion of Filled Polygons
Computer Graphics Forum, 1997Co-Authors: Chengfu Yao, Jon G. RokneAbstract:This paper is motivated by a special Linear Interpolation problem encountered in scan-line algorithms for scan-conversion of filled polygons. rounding-up integral Linear Interpolation is defined and its efficient computation is discussed. The paper then incorporates rounding-up integral Linear Interpolation into a scan-line algorithm for filled polygons, and it discusses the implementation of the algorithm. This approach has the advantage of only requiring integer arithmetic in the calculations. Furthermore, the approach provides a unified treatment for calculating span extrema for left and right edges of the polygon that guarantees the mutual exclusiveness of the ownership of boundary pixels of two filled polygons sharing an edge.
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Bi-directional incremental Linear Interpolation
Computers & Graphics, 1996Co-Authors: Chengfu Yao, Jon G. RokneAbstract:Abstract Two algorithms for incremental Linear Interpolation are developed. The first algorithm is a double-step bi-directional Linear Interpolation algorithm which is derived based on a previously developed double-step Linear Interpolation algorithm. The second algorithm carries the ideas of the first algorithm further to develop a double-step bi-directional run-length slice line drawing algorithm.
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Double-step incremental Linear Interpolation
ACM Transactions on Graphics (TOG), 1992Co-Authors: Jon G. Rokne, Y. RaoAbstract:A two-step incremental Linear Interpolation algorithm is derived and analyzed. It is shown that the algorithm is correct, that it is reversible, and that it is faster than previous single-step algorithms. An example is given of the execution of the algorithm.
M.r.k. Khansari - One of the best experts on this subject based on the ideXlab platform.
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Linear Interpolation lattice for nonstationary signals
IEEE Transactions on Signal Processing, 1993Co-Authors: M.r.k. Khansari, Alberto Leon-garciaAbstract:A ladder algorithm for Linear Interpolation of nonstationary signals is developed. The algorithm is based on the sliding-window least-squares method and can be implemented using a lattice structure. Furthermore, by assuming that the input signal is stationary, the number of parameters required to be calculated is reduced. The lattice structure in the case of stationary input is also presented. >
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A fast algorithm for optimal Linear Interpolation
IEEE Transactions on Signal Processing, 1993Co-Authors: M.r.k. Khansari, Alberto Leon-garciaAbstract:A fast algorithm for computing the optimal Linear Interpolation filter is developed. The algorithm is based on the Sherman-Morrison inversion formula for symmetric matrices. The relationship between the derived algorithm and the Levinson algorithm is illustrated. It is shown that the new algorithm, in comparison with the well-known algorithms, requires fewer multiplications and hence is of lower complexity. >
Michael Unser - One of the best experts on this subject based on the ideXlab platform.
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Linear Interpolation revitalized
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2004Co-Authors: Thierry Blu, P. Thevenaz, Michael UnserAbstract:We present a simple, original method to improve piecewise-Linear Interpolation with uniform knots: we shift the sampling knots by a fixed amount, while enforcing the Interpolation property. We determine the theoretical optimal shift that maximizes the quality of our shifted Linear Interpolation. Surprisingly enough, this optimal value is nonzero and close to 1/5. We confirm our theoretical findings by performing several experiments: a cumulative rotation experiment and a zoom experiment. Both show a significant increase of the quality of the shifted method with respect to the standard one. We also observe that, in these results, we get a quality that is similar to that of the computationally more costly "high-quality" cubic convolution.
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how a simple shift can significantly improve the performance of Linear Interpolation
International Conference on Image Processing, 2002Co-Authors: P. Thevenaz, Michael UnserAbstract:We present a simple, original method to improve piecewise Linear Interpolation with uniform knots. We shift the sampling knots by a fixed amount, while enforcing the Interpolation property. Thanks to a theoretical analysis, we determine the optimal shift that maximizes the quality of our shifted Linear Interpolation. Surprisingly enough, this optimal value is nonzero and it is close to 1/5. We confirm our theoretical findings by performing a cumulative rotation experiment, which shows a significant increase of the quality of the shifted method with respect to the standard one. Most interesting is the fact that we get a quality similar to that of high-quality cubic convolution at the computational cost of Linear Interpolation.
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ICIP (3) - How a simple shift can significantly improve the performance of Linear Interpolation
Proceedings. International Conference on Image Processing, 1Co-Authors: Thierry Blu, P. Thevenaz, Michael UnserAbstract:We present a simple, original method to improve piecewise Linear Interpolation with uniform knots. We shift the sampling knots by a fixed amount, while enforcing the Interpolation property. Thanks to a theoretical analysis, we determine the optimal shift that maximizes the quality of our shifted Linear Interpolation. Surprisingly enough, this optimal value is nonzero and it is close to 1/5. We confirm our theoretical findings by performing a cumulative rotation experiment, which shows a significant increase of the quality of the shifted method with respect to the standard one. Most interesting is the fact that we get a quality similar to that of high-quality cubic convolution at the computational cost of Linear Interpolation.
