Sub-Pixel Estimation Error Cancellation on Area-Based Matching

Area-based image matching and sub-pixel displacement estimation using similarity measures are common methods that are used in various fields. Sub-pixel estimation using parabola fitting over three points with their similarity measures is also a common method to increase the matching resolution. However, few investigations or studies have explored the characteristics of this estimation.This study analyzed sub-pixel estimation error using two different types of matching model. Our analysis demonstrates that the estimation contains a systematic error depending on image characteristics, the similarity function, and the fitting function. This error causes some inherently problematic phenomena such as the so-called pixel-locking effect, by which the estimated positions tend to be biased toward integer values. We also show that there are good combinations of the similarity functions and fitting functions.In addition, we propose a new algorithm to greatly reduce sub-pixel estimation error. This method is independent of the similarity measure and the fitting function. Moreover, it is quite simple to implement. The advantage of our novel method is confirmed through experiments using different types of images.

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