Interpolatory Estimation of Multi-Dimensional Orthogonal Expansions with Stochastic Coefficients

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概要

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• Relating to the problem of suppressing the immanent redundancy contained in an image with out vitiating the quality of the resultant approximation, the interpolation of multi-dimensional signal is widely discussed [5]. The minimization of the approximation error is one of the important problems in this field. In this paper, we establish the optimum interpolatory approximation of multi-dimensional orthogonal expansions. The proposed approximation is superior, in some sense, to all the linear and the nonlinear approximations using a wide class of measures of error and the same generalized moments of these signals. Further, in the fields of information processing, we sometimes consider the orthonormal development of an image each coefficient of which represents the principal feature of the image. The selection of the orthonormal bases becomes important in this problem. The Fisher's criterion is a powerful tool for this class of problems called declustering. In this paper, we will make some remarks to the problem of optimizing the Fisher's criterion under the condition that the quality of the approximation is maintained.

• 社団法人電子情報通信学会の論文
• 1994-05-25

著者

• Sa‐nguankotchakorn S

  Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology

• Kida Takuro

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• Sa-Nguankotchakorn Somsak

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• Jenkins Kenneth

  Coordinated Science Laboratory, University of Illinois

• Jenkins Kenneth

  Coordinated Science Laboratory University Of Illinois

• Kida Takuro

  Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology

関連論文

• Interpolatory Estimation of Multi-Dimensional Orthogonal Expansions with Stochastic Coefficients
• The Optimum Approximate Restoration of Multi-Dimensional Signals Using the Prescribed Analysis or Synthesis Filter Bank
• On Restoration and Approximation of Multi-Dimensional Signals Using Sample Values of Transformed Signals (Special Section on Surveys of Researches in CAS Fields in the Last Two Decadeses, II(
• The Optimum Approximation of Multi-Dimensional Signals Based on the Quantized Sample Values of Transformed Signals

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