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Image Processing and Reconstructing Using Principal Components Analysis
Abstract
Principal components analysis (PCA) is a process of identifying image sequences in an effective way to find differences and similarities of information. Generally, it is very hard to find information from the high dimensional messages, where the graphical representation is not available. In this regard, the PCA is an important statistical procedure for analyzing data and find the reduce number of desired sequences. Essentially, the correlated sequences are transformed into a set of uncorrelated ones which are arranged by reducing variability. The PCA can reduce the computational complexity after reduction dimensions. This paper, we use an original image of dimension 408x350. By applying PCA, we reduced the original dimension to 408x57. Finally, we are remonstrated the original image by using selected features vectors.
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