Algorithms for subpixel registration
Computer Vision, Graphics, and Image Processing
Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetric Sub-Pixel Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Equivalence of subpixel motion estimators based on optical flow and block matching
ISCV '95 Proceedings of the International Symposium on Computer Vision
Extension of phase correlation to subpixel registration
IEEE Transactions on Image Processing
On convergence of the Horn and Schunck optical-flow estimation method
IEEE Transactions on Image Processing
Multi-Parameter Simultaneous Estimation on Area-Based Matching
International Journal of Computer Vision
A portable stereo vision system for whole body surface imaging
Image and Vision Computing
Accuracy of sub-pixel estimation in area-based matching
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Performance evaluation using mandelbrot images for images registration algorithms
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Optical flow estimation with prior models obtained from phase correlation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Mixing remote locations using shared screen as virtual stage
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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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.