Adaptive Missing Texture Reconstruction Method Based on Kernel Canonical Correlation Analysis with a New Clustering Scheme
Adaptive Missing Texture Reconstruction Method Based on Kernel Canonical Correlation Analysis with a New Clustering Scheme
复制标题
DOI：
10.1587/transfun.e92.a.1950
复制
发表时间：
2009-08
期刊：
影响因子：
--
通讯作者：
Takahiro Ogawa;M. Haseyama
中科院分区：
文献类型：
--
作者：
Takahiro Ogawa;M. Haseyama
本文提出了一种基于核典型相关分析（CCA）和新的聚类方案的自适应纹理缺失重建方法。该方法从目标图像中的已知部分估计出缺失区域及其相邻区域之间的相关性，实现缺失纹理的重建。为了获得这种相关性，核CCA被应用到每个类包含相同类型的纹理，并选择最佳的结果为目标丢失区域。具体地，监视在上述基于核CCA的重建过程中引起的误差的新方法使得能够选择最优结果。这种方法提供了一个解决方案，在传统的方法，不能执行自适应重建的目标纹理，由于丢失的强度的问题。因此，所有的丢失的纹理被成功地估计的最佳聚类的相关性，这提供了准确的重建同类纹理。此外，所提出的方法可以获得更准确的相关性比我们以前的工作，更成功的重建性能可以预期。实验结果表明，令人印象深刻的改进所提出的重建技术比以前报道的重建技术。
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.