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Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition

机译:利用相关图像的低分辨率特性提高本征空间分解的计算效率

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Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high-resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known a priori how this will affect the quality of the resulting eigendecomposition. The work presented here provides an analysis of how different resolution reduction techniques affect the eigendecomposition. A computationally efficient algorithm for calculating the eigendecomposition based on this analysis is proposed. Examples show that this algorithm performs well on arbitrary video sequences.
机译:本征分解是在许多计算机视觉和机器人技术应用程序中对一组相关图像执行的常用技术。不幸的是,当处理非常高分辨率的图像时,本征分解的计算变得非常昂贵。虽然降低图像的分辨率将减少计算费用,但先验地知道这将如何影响所得本征分解的质量,这是先验的。本文介绍的工作提供了对不同分辨率降低技术如何影响本征分解的分析。提出了一种基于该分析的计算特征分解的高效计算算法。实例表明,该算法在任意视频序列上表现良好。

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