Robust Projection onto Normalized Eigenspace Using Relative Residual Analysis and Optimal Partial Projection

Fumihiko SAKAUE; Takeshi SHAKUNAGA

Robust Projection onto Normalized Eigenspace Using Relative Residual Analysis and Optimal Partial Projection

Fumihiko SAKAUE, Takeshi SHAKUNAGA

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The present paper reports a robust projection onto eigenspace that is based on iterative projection. The fundamental method proposed in Shakunaga and Sakaue and involves iterative analysis of relative residual and projection. The present paper refines the projection method by solving linear equations while taking noise ratio into account. The refinement improves both the efficiency and robustness of the projection. Experimental results indicate that the proposed method works well for various kinds of noise, including shadows, reflections and occlusions. The proposed method can be applied to a wide variety of computer vision problems, which include object/face recognition and image-based rendering.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.1 pp.34-41

Publication Date: 2004/01/01

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Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)

Category: Reconstruction

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Fumihiko SAKAUE, Takeshi SHAKUNAGA, "Robust Projection onto Normalized Eigenspace Using Relative Residual Analysis and Optimal Partial Projection" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 1, pp. 34-41, January 2004, doi: .
Abstract: The present paper reports a robust projection onto eigenspace that is based on iterative projection. The fundamental method proposed in Shakunaga and Sakaue and involves iterative analysis of relative residual and projection. The present paper refines the projection method by solving linear equations while taking noise ratio into account. The refinement improves both the efficiency and robustness of the projection. Experimental results indicate that the proposed method works well for various kinds of noise, including shadows, reflections and occlusions. The proposed method can be applied to a wide variety of computer vision problems, which include object/face recognition and image-based rendering.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e87-d_1_34/_p

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@ARTICLE{e87-d_1_34,
author={Fumihiko SAKAUE, Takeshi SHAKUNAGA, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Projection onto Normalized Eigenspace Using Relative Residual Analysis and Optimal Partial Projection},
year={2004},
volume={E87-D},
number={1},
pages={34-41},
abstract={The present paper reports a robust projection onto eigenspace that is based on iterative projection. The fundamental method proposed in Shakunaga and Sakaue and involves iterative analysis of relative residual and projection. The present paper refines the projection method by solving linear equations while taking noise ratio into account. The refinement improves both the efficiency and robustness of the projection. Experimental results indicate that the proposed method works well for various kinds of noise, including shadows, reflections and occlusions. The proposed method can be applied to a wide variety of computer vision problems, which include object/face recognition and image-based rendering.},
keywords={},
doi={},
ISSN={},
month={January},}

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TY - JOUR
TI - Robust Projection onto Normalized Eigenspace Using Relative Residual Analysis and Optimal Partial Projection
T2 - IEICE TRANSACTIONS on Information
SP - 34
EP - 41
AU - Fumihiko SAKAUE
AU - Takeshi SHAKUNAGA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2004
AB - The present paper reports a robust projection onto eigenspace that is based on iterative projection. The fundamental method proposed in Shakunaga and Sakaue and involves iterative analysis of relative residual and projection. The present paper refines the projection method by solving linear equations while taking noise ratio into account. The refinement improves both the efficiency and robustness of the projection. Experimental results indicate that the proposed method works well for various kinds of noise, including shadows, reflections and occlusions. The proposed method can be applied to a wide variety of computer vision problems, which include object/face recognition and image-based rendering.
ER -