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PROJECT TITLE :

Matching of Large Images Through Coupled Decomposition - 2015

ABSTRACT:

In this paper, we address the problem of quick and correct extraction of points that correspond to the same location (named tie-points) from pairs of huge-sized images. First, we have a tendency to conduct a theoretical analysis of the performance of the complete-image matching approach, demonstrating its limitations when applied to massive images. Subsequently, we have a tendency to introduce a novel technique to impose spatial constraints on the matching process without using subsampled versions of the reference and also the target image, that we have a tendency to name coupled image decomposition. This technique splits images into corresponding subimages through a method that's theoretically invariant to geometric transformations, additive noise, and global radiometric variations, along with being strong to local changes. After presenting it, we demonstrate how coupled image decomposition will be used both for image registration and for automatic estimation of epipolar geometry. Finally, coupled image decomposition is tested on a information set consisting of many planetary images of different size, varying from but one megapixel to many lots of megapixels. The reported experimental results, that includes comparison with full-image matching and state-of-the-art techniques, demonstrate the substantial computational value reduction which will be achieved when matching large images through coupled decomposition, while not at the identical time compromising the overall matching accuracy.


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Image Registration Full-Image Matching Approach Tie-Points Large-Sized Images Additive Noise Global Radiometric Differences Coupled Image Decomposition Automatic Estimation Spatial Constraints Epipolar Geometry Data Set Computational Cost Reduction Matching Accuracy
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