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Takahiro OGAWA, Miki HASEYAMA, "POCS-Based Texture Reconstruction Method Using Clustering Scheme by Kernel PCA" in IEICE TRANSACTIONS on Fundamentals, vol. E90-A, no. 8, pp. 1519-1527, August 2007, doi:10.1093/ietfec/e90-a.8.1519.
Abstract:A new framework for reconstruction of missing textures in digital images is introduced in this paper. The framework is based on a projection onto convex sets (POCS) algorithm including a novel constraint. In the proposed method, a nonlinear eigenspace of each cluster obtained by classification of known textures within the target image is applied to the constraint. The main advantage of this approach is that the eigenspace can approximate the textures classified into the same cluster in the least-squares sense. Furthermore, by monitoring the errors converged by the POCS algorithm, a selection of the optimal cluster to reconstruct the target texture including missing intensities can be achieved. This POCS-based approach provides a solution to the problem in traditional methods of not being able to perform the selection of the optimal cluster due to the missing intensities within the target texture. Consequently, all of the missing textures are successfully reconstructed by the selected cluster's eigenspaces which correctly approximate the same kinds of textures. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.8.1519/_p

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@ARTICLE{e90-a_8_1519,
author={Takahiro OGAWA, Miki HASEYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={POCS-Based Texture Reconstruction Method Using Clustering Scheme by Kernel PCA},
year={2007},
volume={E90-A},
number={8},
pages={1519-1527},
abstract={A new framework for reconstruction of missing textures in digital images is introduced in this paper. The framework is based on a projection onto convex sets (POCS) algorithm including a novel constraint. In the proposed method, a nonlinear eigenspace of each cluster obtained by classification of known textures within the target image is applied to the constraint. The main advantage of this approach is that the eigenspace can approximate the textures classified into the same cluster in the least-squares sense. Furthermore, by monitoring the errors converged by the POCS algorithm, a selection of the optimal cluster to reconstruct the target texture including missing intensities can be achieved. This POCS-based approach provides a solution to the problem in traditional methods of not being able to perform the selection of the optimal cluster due to the missing intensities within the target texture. Consequently, all of the missing textures are successfully reconstructed by the selected cluster's eigenspaces which correctly approximate the same kinds of textures. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.},
keywords={},
doi={10.1093/ietfec/e90-a.8.1519},
ISSN={1745-1337},
month={August},}

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TY - JOUR
TI - POCS-Based Texture Reconstruction Method Using Clustering Scheme by Kernel PCA
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1519
EP - 1527
AU - Takahiro OGAWA
AU - Miki HASEYAMA
PY - 2007
DO -10.1093/ietfec/e90-a.8.1519
JO - IEICE TRANSACTIONS on Fundamentals
SN -1745-1337
VL - E90-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2007
AB -A new framework for reconstruction of missing textures in digital images is introduced in this paper. The framework is based on a projection onto convex sets (POCS) algorithm including a novel constraint. In the proposed method, a nonlinear eigenspace of each cluster obtained by classification of known textures within the target image is applied to the constraint. The main advantage of this approach is that the eigenspace can approximate the textures classified into the same cluster in the least-squares sense. Furthermore, by monitoring the errors converged by the POCS algorithm, a selection of the optimal cluster to reconstruct the target texture including missing intensities can be achieved. This POCS-based approach provides a solution to the problem in traditional methods of not being able to perform the selection of the optimal cluster due to the missing intensities within the target texture. Consequently, all of the missing textures are successfully reconstructed by the selected cluster's eigenspaces which correctly approximate the same kinds of textures. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.
ER -