Precise Geometric Registration by Blur Estimation for Vision-based Augmented Reality

Abstract

This paper proposes an accurate geometric registration method by estimating blur effects from a degraded image with image markers for augmented reality. A small and inexpensive camera used in augmented reality systems sometimes captures degraded images because its focus and/or iris are fixed. This degradation of a captured image affects the accuracy of the detected positions of feature points in the image. The proposed method improves the accuracy of the estimated camera position and posture by estimating blur effects from the captured image, and by correcting the detected positions of feature points through the results. The effectiveness of the method is confirmed through experiments of corner estimation from simulated images and extrinsic camera parameter estimation from real images.