The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 새로운 클러스터링 방식을 사용하여 커널 CCA(Canonical Correlation Analysis)를 기반으로 누락된 텍스처를 적응적으로 재구성하는 방법을 제시합니다. 제안된 방법은 대상 영상 내 알려진 부분으로부터 각각 누락된 영역과 그 주변 영역에 해당하는 두 영역 간의 상관 관계를 추정하고 누락된 텍스처의 재구성을 실현합니다. 이러한 상관관계를 얻기 위해 동일한 종류의 텍스처를 포함하는 각 클러스터에 커널 CCA를 적용하고 대상 누락 영역에 대해 최적의 결과를 선택합니다. 구체적으로, 위의 커널 CCA 기반 재구성 과정에서 발생하는 오류를 모니터링하는 새로운 접근 방식을 통해 최적의 결과를 선택할 수 있습니다. 이 접근 방식은 누락된 강도로 인해 대상 텍스처의 적응형 재구성을 수행할 수 없는 기존 방법의 문제에 대한 솔루션을 제공합니다. 결과적으로 누락된 모든 텍스처는 최적 클러스터의 상관 관계에 의해 성공적으로 추정되며, 이는 동일한 종류의 텍스처를 정확하게 재구성합니다. 또한 제안한 방법은 기존 연구보다 더 정확하게 상관관계를 얻을 수 있어 보다 성공적인 재구성 성능을 기대할 수 있다. 실험 결과는 이전에 보고된 재구성 기술에 비해 제안된 재구성 기술의 인상적인 개선을 보여줍니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Takahiro OGAWA, Miki HASEYAMA, "Adaptive Missing Texture Reconstruction Method Based on Kernel Canonical Correlation Analysis with a New Clustering Scheme" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 8, pp. 1950-1960, August 2009, doi: 10.1587/transfun.E92.A.1950.
Abstract: In this paper, a method for adaptive reconstruction of missing textures based on kernel canonical correlation analysis (CCA) with a new clustering scheme is presented. The proposed method estimates the correlation between two areas, which respectively correspond to a missing area and its neighboring area, from known parts within the target image and realizes reconstruction of the missing texture. In order to obtain this correlation, the kernel CCA is applied to each cluster containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above kernel CCA-based reconstruction process enables selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to missing intensities. Consequently, all of the missing textures are successfully estimated by the optimal cluster's correlation, which provides accurate reconstruction of the same kinds of textures. In addition, the proposed method can obtain the correlation more accurately than our previous works, and more successful reconstruction performance can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1950/_p
부
@ARTICLE{e92-a_8_1950,
author={Takahiro OGAWA, Miki HASEYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Missing Texture Reconstruction Method Based on Kernel Canonical Correlation Analysis with a New Clustering Scheme},
year={2009},
volume={E92-A},
number={8},
pages={1950-1960},
abstract={In this paper, a method for adaptive reconstruction of missing textures based on kernel canonical correlation analysis (CCA) with a new clustering scheme is presented. The proposed method estimates the correlation between two areas, which respectively correspond to a missing area and its neighboring area, from known parts within the target image and realizes reconstruction of the missing texture. In order to obtain this correlation, the kernel CCA is applied to each cluster containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above kernel CCA-based reconstruction process enables selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to missing intensities. Consequently, all of the missing textures are successfully estimated by the optimal cluster's correlation, which provides accurate reconstruction of the same kinds of textures. In addition, the proposed method can obtain the correlation more accurately than our previous works, and more successful reconstruction performance can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.},
keywords={},
doi={10.1587/transfun.E92.A.1950},
ISSN={1745-1337},
month={August},}
부
TY - JOUR
TI - Adaptive Missing Texture Reconstruction Method Based on Kernel Canonical Correlation Analysis with a New Clustering Scheme
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1950
EP - 1960
AU - Takahiro OGAWA
AU - Miki HASEYAMA
PY - 2009
DO - 10.1587/transfun.E92.A.1950
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2009
AB - In this paper, a method for adaptive reconstruction of missing textures based on kernel canonical correlation analysis (CCA) with a new clustering scheme is presented. The proposed method estimates the correlation between two areas, which respectively correspond to a missing area and its neighboring area, from known parts within the target image and realizes reconstruction of the missing texture. In order to obtain this correlation, the kernel CCA is applied to each cluster containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above kernel CCA-based reconstruction process enables selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to missing intensities. Consequently, all of the missing textures are successfully estimated by the optimal cluster's correlation, which provides accurate reconstruction of the same kinds of textures. In addition, the proposed method can obtain the correlation more accurately than our previous works, and more successful reconstruction performance can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.
ER -