Eigenmosaic: Reconstruction of Images Using PCA Based Mosaic Method

In this article we have introduced new techniques for generating compound images, or mosaics, which include reconstruction of main images using homomorphous square patches. These patches are extracted from another image called Key Image and are sorted next to each other in order to produce the image. To perform that, criterions like color level average, variance and histograms are used due to their important role in describing the façade of an image. PCA is a valuable statistical method in pattern recognition among the data; that can be used an images as well. These methods basically help us separate and analyze image parameters. PCA implementation results will be used as filters for the main image leading to extraction of constructor lines and different parts of image construction texture. These extracted parameters from the main and key image can be a local or global reference to select parts in order to produce the mosaic. The results of experiments has been shown that our method base on PCA is better than other methods.

Keywords:

Mosaic , en , Image Parameters Extraction and Analysis , Analogy , PCA