High Accuracy Homography Computation without Iterations
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We present highly accurate least-squares (LS) alternatives to the theoretically optimal maximum likelihood (ML) estimator for homographies between two images. Unlike ML, our estimators are non-iterative and yield solutions even in the presence of large noise. By rigorous error analysis, we derive a "hyperaccurate" estimator which is unbiased up to second order noise terms. Then, we introduce a computational simplification, which we call "Taubin approximation", without incurring a loss in accuracy. We experimentally demonstrate that our estimators have accuracy surpassing the traditional LS estimator and comparable to the ML estimator.
- Memoirs of the Faculty of Engineering, Okayama University
Memoirs of the Faculty of Engineering, Okayama University (44), 50-59, 2010-01
Faculty of Engineering, Okayama University