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Sliding Window Recursive HAPCA for 3D Image Decomposition

机译：滑动窗口递归HAPCA用于3D图像分解

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摘要

The famous method Principal Components Analysis (PCA) is the basic approach for decomposition of 3D tensor images (for example, multi- and hyper-spectral, multi-view, computer tomography, video, etc.). As a result of the processing, their information redundancy is significantly reduced. This is of high importance for the efficient compression and for the reduction of the features space needed, when object recognition or search is performed. The basic obstacle for the wide application of PCA is the high computational complexity. One of the approaches to overcome the problem is to use algorithms, based on the recursive PCA. The well-known methods for recursive PCA are aimed at the processing of sequences of images, represented as non-overlapping groups of vectors. In this work is proposed new method, called Sliding Recursive Hierarchical Adaptive PCA, based on image sequence processing in a sliding window. The new method decreases the number of calculations needed, and permits parallel implementation. The results obtained from the algorithm simulation, confirm its efficiency. The lower computational complexity of the new method facilitates its application in the real-time processing of 3D tensor images.
机译：着名的方法主要成分分析（PCA）是用于分解3D张量图像的基本方法（例如，多谱，多视图，计算机断层扫描，视频等）。由于处理的结果，它们的信息冗余显着降低。当执行对象识别或搜索时，这对于有效压缩和需要减少特征空间的重要性高度重要性。广泛应用PCA的基本障碍是高计算复杂性。克服问题的方法之一是基于递归PCA使用算法。递归PCA的众所周知的方法旨在处理图像序列，表示为非重叠载体组。在这项工作中，基于在滑动窗口中的图像序列处理，提出了称为滑动递归层级自适应PCA的新方法。新方法降低所需的计算次数，并允许并行实现。从算法模拟中获得的结果，确认其效率。新方法的较低计算复杂性有助于其在3D张量图像的实时处理中的应用。

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