Journal Article10.1002/SCJ.10506
Significance and attributes of subpixel estimation on area-based matching
Masao Shimizu,Masatoshi Okutomi +1 more
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TL;DR: Characteristics of subpixel estimation error were clarified using a simple analytical model and it has been shown that there are correct combinations of the similarity functions and the fitting functions.
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Abstract: Area-based matching and subpixel displacement estimation using similarity measures are common methods used in various fields. Subpixel estimation using parabola fitting over three points with their similarity measures is also a common method to increase matching resolution. However, such estimation can contain systematic error depending on image characteristics, the similarity function, and the fitting function used for subpixel estimation. In this paper, characteristics of subpixel estimation error were clarified using a simple analytical model. Although this model is quite simple, it allows us to make a theoretical analysis for general images without assuming any specific image pattern. Through this analysis, it has been shown that there are correct combinations of the similarity functions and the fitting functions. Also, the so-called “pixel-locking” effect, by which estimated positions tend to be biased toward integer values, has been explained. Finally, analyses were verified with an experiment using real images. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(12): 1–10, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10506
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Two-dimensional simultaneous subpixel estimation for area-based matching
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Precise sub-pixel estimation on area-based matching
Masao Shimizu,Masatoshi Okutomi +1 more
- 07 Jul 2001
TL;DR: This paper analyzes the sub-pixel estimation error by using an approximate image function and three kinds of similarity measures for matching and proposes a new algorithm to greatly reduce sub- pixel estimation error.
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Precise subpixel estimation on area‐based matching
Masao Shimizu,Masatoshi Okutomi +1 more
TL;DR: This paper analyzes the subpixel estimation error by using an approximate image function and three kinds of similarity measures for matching and proposes a new algorithm to greatly reduce subpixels estimation error.
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