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Proceedings Paper

Diffusion and NCC combined image registration
Author(s): Bingcheng Li
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Paper Abstract

Image registration is a process of transforming a data set from one coordinate system into another. There are two typical approaches for image registration: Feature point match based and Area similarity comparison based. The feature point match based approach, using points to establish the correspondence between two images, is relatively fast, but it involves feature extractions and parameter selection to create feature points. Feature extractions involve derivatives which are ill-posed problems and may lead to robustness issues. The area similarity comparison based approach compares intensity patterns using a correlation metric such as normalized cross correlation (NCC). Since it does not require feature extraction, is simple and not sensitive to noise. However its computational cost is high. Even when some fast techniques like FFT are used to reduce the computational cost, the implementation is still time consuming. In this paper, we propose a diffusion equation and normalized cross correlation (NCC) combined method to perform robust image registration with low computational cost. We first apply the diffusion equation to two images received from two sensors (or the same sensor) and allow these two images to evolve by this diffusion equation. Based on the characteristics of evolutions, we select a very small percentage of stable points in the first image and perform the normalized cross correlation to the second image at each transformation point. The highest NCC point provides the transformation parameters for registering these two images. This new method is resistant to noise since the evolution of the diffusion equation reduces noise and it chooses only stable points for the NCC computation. Furthermore, the new method is computationally efficient since only a small percentage of pixels involve in the transformation estimation. Finally, the experiments for video motion estimation and image registration are provided to demonstrate that the new method is able to estimate the registration transformation reliably in real time.

Paper Details

Date Published: 16 May 2013
PDF: 10 pages
Proc. SPIE 8737, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2013, 87370I (16 May 2013); doi: 10.1117/12.2014296
Show Author Affiliations
Bingcheng Li, Lockheed Martin MST (United States)

Published in SPIE Proceedings Vol. 8737:
Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2013
Kenneth L. Bernier; Jeff J. Güell, Editor(s)

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